Portable Monitoring Devices and Methods of Operating Same

ABSTRACT

The present inventions, in one aspect, are directed to an activity monitoring system including a portable activity monitoring device comprising a housing having a physical size and shape that is adapted to couple to the user&#39;s body, a motion sensor, disposed in the housing, to generate data which is representative of the user&#39;s motion, and an altitude sensor, disposed in the housing, to generate data which is representative of the change in the user&#39;s altitude. The system further includes a display to output data which is representative of a badge, wherein the badge is representative of an achievement computed using motion and/or altitude sensor data. The monitoring device may also include a physiological sensor to generate data which is representative of a user&#39;s physiological condition. Here, the display outputs data which is representative of a badge wherein the badge is representative of a physiological achievement.

RELATED APPLICATION

This application is a divisional of U.S. patent application Ser. No.13/667,242, filed Nov. 2, 2012, entitled “Portable Monitoring Devicesand Methods of Operating Same” (still pending), which is a divisional ofU.S. patent application Ser. No. 13/469,033, filed May 10, 2012,entitled “Portable Monitoring Devices and Methods of Operating Same”(U.S. Pat. No. 8,311,770), which is a divisional of U.S. patentapplication Ser. No. 13/249,155, filed Sep. 29, 2011, entitled “PortableMonitoring Devices and Methods of Operating Same” (now U.S. Pat. No.8,180,592), which is a divisional of U.S. patent application Ser. No.13/156,304, filed on Jun. 8, 2011, entitled “Portable Monitoring Devicesand Methods of Operating Same”, filed on September (still pending). Thisnon-provisional application, and the aforementioned non-provisionalapplications, claim priority to U.S. Provisional App. No. 61/388,595,entitled “Portable Monitoring Devices and Methods of Operating Same”,filed Sep. 30, 2010, and U.S. Provisional App. No. 61/390,811, entitled“Portable Monitoring Devices and Methods of Operating Same”, filed Oct.7, 2010; the contents of these U.S. Provisional applications areincorporated by reference herein in their entirety.

INTRODUCTION

The present inventions relate to portable monitoring devices, and methodof operating and controlling same, wherein the portable monitoringdevices include an altitude sensor, motion sensor and processingcircuitry to calculate, assess and/or determine calorie burn and/orother activity-related quantities of the user (for example, a human ornon-human animal such as a dog, cat or horse). In these aspects, thepresent inventions employ the altitude sensor data and the motion sensordata to calculate, assess and/or determine the calorie burn and/or otheractivity-related quantities of the user (for example, number of stepsand/or stairs, number of stair flights, elevation gain/loss fromambulatory and/or non-ambulatory locomotion, absolute elevation,elevation and/or activity points, activity intensity, distance traveledand/or pace, number of swim strokes and/or kicks, strokes per lap, laptime, pace and/or distance, number of pedal rotations of a bicycle, armor wheel rotation of a wheelchair, heart rate, heart rate variability,respiration rate, stress levels, skin temperature, body temperature). Inthe following disclosure, use of the term “activity” includes sedentaryand nonsedentary activities. As such, the present inventions also may beused to monitor activities related to sleeping, lying, sitting, andstanding stationary and may provide corresponding metrics (for example,time asleep, the onset, duration, and number of awakenings whileattempting to sleep, the time spent in various stages of sleep, sleeplatency, sleep efficiency and other sleep quality parameters, thepresence of sleep apnea and other diagnostic measures, time spent in aprone non-standing state, and resting heart rate).

In other aspects, the portable monitoring device of the presentinventions may include a physiological sensor, in addition to thealtitude sensor, motion sensor and processing circuitry. In theseaspects, the present inventions employ the physiological sensor data,altitude sensor data and motion sensor data to calculate, assess and/ordetermine the calorie burn and/or such other activity-related quantitiesof the user.

In certain aspects the processing circuitry is partially or whollydisposed external to the portable monitoring device wherein the externalprocessing circuitry receives partially processed or “raw” sensor data.Here, the external processing circuitry partially or wholly calculates,assesses and/or determines the calorie burn and/or otheractivity-related quantities of the user.

Notably, the present inventions also relate to techniques or methods ofcalculating, assessing and/or determining the calorie burn and/or otheractivity-related quantities of the user based on or using sensor dataacquired by a portable monitoring device, for example, devices accordingto any of the of the present inventions.

BRIEF DESCRIPTION OF THE DRAWINGS

In the course of the detailed description to follow, reference will bemade to the attached drawings. These drawings show different aspects ofthe present inventions and, where appropriate, reference numeralsillustrating like structures, components, materials and/or elements indifferent figures are labeled similarly. It is understood that variouscombinations of the structures, components, and/or elements, other thanthose specifically shown, are contemplated and are within the scope ofthe present inventions.

Moreover, there are many inventions described and illustrated herein.The present inventions are neither limited to any single aspect norembodiment thereof, nor to any combinations and/or permutations of suchaspects and/or embodiments. Moreover, each of the aspects of the presentinventions, and/or embodiments thereof, may be employed alone or incombination with one or more of the other aspects of the presentinventions and/or embodiments thereof. For the sake of brevity, certainpermutations and combinations are not discussed and/or illustratedseparately herein.

FIGS. 1A-1L are block diagram representations of exemplary portablemonitoring devices, according to at least certain aspects of certainembodiments of the present inventions, wherein the portable monitoringdevices, according to at least certain aspects of certain embodiments ofthe present inventions, include processing circuitry, one or morealtitude sensors, one or more motion sensors and, in certainembodiments, one or more physiological sensors, one or more modesensors, transmitter circuitry and/or receiver circuitry;

FIGS. 1M-1X are block diagram representations of exemplary portablemonitoring devices, according to at least certain aspects of certainembodiments of the present inventions, wherein the portable monitoringdevices, according to at least certain aspects of certain embodiments ofthe present inventions, include one or more motion sensors, and, incertain embodiments, may also include processing circuitry, transmittercircuitry and/or receiver circuitry, one or more physiological sensors,and one or more mode sensors;

FIG. 2 is a block diagram representation of processing circuitry tocalculate, assess and/or determine the calorie burn of the user based onsensor data; the processing circuitry may include memory (for example,Flash memory, DRAM and/or SRAM) to store, for example, (i) sensor dataand (ii) information which is representative of calorie burn—forexample, cumulative and/or over time; notably, the processing circuitrymay be discrete or integrated logic, and/or one or more state machines,processors (suitably programmed) and/or field programmable gate arrays(or combinations of the aforementioned); indeed, any circuitry (forexample, discrete or integrated logic, state machine(s), processor(s)(suitably programmed) and/or field programmable gate array(s) (orcombinations of the aforementioned)) now known or later developed may beemployed to calculate, determine, assess and/or determine the calorieburn of the user based on sensor data;

FIGS. 3A and 3B are block diagram representations of exemplary motionsensors which include, for example, one or more accelerometers,gyroscopes, compasses, switches (for example, mechanical), piezoelectricfilm and/or pedometers to determine, calculate and/or detect one or moresteps of the user; notably, the exemplary motion sensor may beincorporated in any of the exemplary portable monitoring devices;

FIG. 3C is a block diagram representation of one or more altitudesensor(s) that may be incorporated in the exemplary portable monitoringdevices according to any of the exemplary embodiments of the presentinventions;

FIGS. 4A-4D, 4F, 4H-4J, and 4M-4R are flowcharts of exemplary processesof calculating, obtaining, assessing and/or determining calorie burn ofthe user based on certain sensor data, according to certain aspects ofthe present inventions;

FIGS. 4E and 4G are flowcharts of exemplary processes for calculating,obtaining, assessing and/or determining other activity-related metricsincluding, for example, steps taken by the user based on certain sensordata, according to certain aspects of the present inventions;

FIGS. 4K and 4L are flowcharts exemplary of processes for calculating,obtaining, assessing and/or determining the activity state of the user(for example, walking or running on relative flat or level ground,traversing stairs, on an escalator or in an elevator, traversing a hillor the like), based on certain sensor data including the attitude sensordata and/or physiological sensor data, according to certain aspects ofthe invention; notably, hereinafter the activity state of the user maybe indicated as the “user state”;

FIGS. 5A and 5B are block diagram representations of the motion sensorin combination with flowcharts of exemplary processes of calculating,obtaining, assessing and/or determining calorie burn of the user basedon speed data, according to certain aspects of the present inventions;

FIG. 6 is an example of determining the activity state of the user byevaluating the altitude sensor data based on or using algorithms orprocesses generally illustrated in the flowchart of FIG. 4K whereinaltitude data is depicted in the top panel and acceleration data isdepicted in the bottom panel; in this exemplary data set and activitystate determination, the walking segments are marked A, B, C and aredetermined by the pedometer function with each step marked as a red dotin the bottom panel; drawing indicator 1 identifies a period ofaltimeter measurement artifact that is disregarded because the user hasnot performed any steps; drawing indicator 2 identifies a period ofwalking that includes a 20 ft drop in apparent altitude due to motionartifact—this is disregarded because the user only walked four stepsduring this interval, so Δh/Δstep=−5 ft/step, which is likely nothumanly possible under normal circumstances; drawing indicator 3identifies a period of walking on stairs: 20 steps for a total heightincrease of 10 ft (Δh/Δstep=0.5 ft/step) and is used to update theappropriate activity metrics; drawing indicator 4 identifies a period ofwalking up an escalator: 24 steps over 32 ft (Δh/Δstep=1.3 ft/step) andis used to update the appropriate activity metrics, taking into accountthat the activity was partially assisted; drawing indicator 5 identifiesa period of walking up a hill: 350 steps for a height increase of 33 ft(Δh/Δstep=0.1 ft/step) and is used to update the appropriate activitymetrics;

FIG. 7 is a block diagram representation of one or more physiologicalsensor(s) to determine, sense, detect, assess and/or obtain informationwhich is representative of physiological information of the user,according to at least certain embodiments of the present inventions;notably, the one or more physiological sensor(s) may be incorporated inand/or coupled to the exemplary portable monitoring devices (forexample, physically, electrically and/or optically coupled, includingwired and/or wirelessly coupled) according to at least certainembodiments of the present inventions;

FIGS. 8A-8G are flowcharts of exemplary processes or logic employed bythe processing circuitry (for example, a state machine) to determine,estimate and/or calculate changes in altitude), according to certainaspects of the present inventions;

FIG. 8H is a flowchart of an exemplary process or logic to compute,estimate and/or determine a number of stair steps traversed by the user(for example, the number of upward stair steps), according to certainaspects of the present inventions; notably, in this exemplaryembodiment, when ΔH-S and ΔH-t meet a first criteria, the processingcircuitry determines, calculates and/or estimates an onset of the firststep of the stair sequence;

FIGS. 9A-9D are flowcharts of exemplary processes of controlling,adjusting and/or determining a sampling frequency of the altitude sensor(F_(ALT)) based on or using data which is representative of motion ofthe user (for example, from a motion sensor of the portable monitoringdevice), according to certain aspects of the present inventions; notablyFIG. 9D illustrates an embodiment of the portable monitoring devicewhere sampling of the altitude sensor is determined or triggered basedon or using step events detected by a motion sensor (for example, apedometer), or a maximum time T between samples (whichever occursfirst);

FIGS. 10A-10F are block diagram representations of exemplary userinterfaces of the exemplary portable monitoring devices according to atleast certain embodiments of the present inventions; in these exemplaryillustrative embodiments, the user interface includes output mechanisms(for example, a display and/or speaker) and input mechanism (forexample, switches, a microphone, and vibration and gesture recognitionsensor(s), wherein the user may input data and/or commands); notably,any manner of and/or mechanism for outputting and/or inputting of dataand/or commands (for example, responses to, for example, queries) areintended to fall within the scope of the present inventions;

FIGS. 11A and 11B are flowcharts of exemplary processes of calculating,obtaining, assessing and/or determining calorie burn and otheractivity-related metrics for the user based on, among other things, datafrom one or more mode sensors; notably, with respect to FIG. 11B,“Body-Mounted Ambulation Metrics” includes Output Steps, Pace, Distance,Calorie Burn, Heart Rate, HRV, Activity Intensity, Heart Rate Zones,Altitude Gain, Stair Steps and/or Elevation Points, “Foot-MountedAmbulation Metrics” includes Output Steps, Pace, Distance, Calorie Burn,Activity Intensity, Heart Rate Zones, Altitude Gain, Stair Steps and/orElevation Points, “Bicycle Metrics” includes Output Altitude Gain,Speed, Distance, Cadence, Calorie Burn, Activity Intensity and/orElevation Points, “Wheelchair Metrics” includes Output Altitude Gain,Speed, Distance, Wheel Spins, Cadence, Calorie Burn, Activity Intensityand/or Elevation Points, “Swimming Metrics” includes Output Depth,Speed, Distance, Laps, Lap Time, Strokes, Drift Time, Turnaround Time,Calorie Burn, Stroke Type, Heart Rate, HRV, Activity Intensity and/orHeart Rate Zones, and “Sleeping Metrics” includes Output Sleep Latency,Number, Duration, and Onset of Awakenings, Sleep Apnea Detection,Resting Heart Rate, Calorie Burn, Duration and Onset of Sleep Stages;

FIG. 12A is a block diagram representation of exemplary portablemonitoring devices, according to at least certain aspects of certainembodiments of the present inventions, wherein the portable monitoringdevices, according to at least certain aspects of certain embodiments ofthe present inventions, includes an altitude sensor and a motion sensor,and wherein the processing circuitry is external to the portablemonitoring devices calculates or determines energy and/or calorie “burn”and/or other activity metrics due to activity of the user using altitudeand motion sensor data; notably, other embodiments of the portablemonitoring device of this aspect may also include one or morephysiological sensors, one or more mode sensors, transmitter circuitryand/or receiver circuitry; for example, any portable monitoring deviceof the present inventions may employ or be implemented in any embodimentwhere the processing circuitry is disposed external to the portablemonitoring device;

FIG. 12B is a block diagram representation of exemplary portablemonitoring devices, according to at least certain aspects of certainembodiments of the present inventions, wherein the portable monitoringdevices, according to at least certain aspects of certain embodiments ofthe present inventions, includes an altitude sensor, a motion sensor,and certain processing circuitry—wherein certain other processingcircuitry is external to the portable monitoring devices and theprocessing circuitry, in combination, calculates or determines energyand/or calorie “burn” and/or other activity metrics due to activity ofthe user using altitude and motion sensor data; notably, otherembodiments of the portable monitoring device of this aspect may alsoinclude one or more physiological sensors, one or more mode sensors,transmitter circuitry and/or receiver circuitry; for example, anyportable monitoring device of the present inventions may employ or beimplemented in any embodiment where the processing circuitry is disposedexternal to the portable monitoring device;

FIG. 13 is a cross-sectional representational view of an altitudesensing microelectromechanical system (MEMS) to sense, sample, determineand/or obtain altitude data, according to at least certain aspects ofcertain embodiments of the present inventions; notably, the altitudesensing MEMS of FIG. 13 may be incorporated in any of the exemplaryportable monitoring devices of the present inventions;

FIG. 14 illustrates an exemplary gesture of the portable monitoringdevice that is mostly contained in the orthogonal plane to thegravitational vector which may be used as a user interface mechanism(e.g., to navigate a menu system);

FIGS. 15A-15C are block diagram representations of exemplary portablemonitoring devices including energy storage device (for example, abattery and/or ultracapacitor(s)) and/or energy harvesting circuitrywherein energy acquired, obtained and/or generated by the energyharvesting circuitry is employed to immediately power the device orstored in energy storage device; according to at least certainembodiments of the present inventions;

FIGS. 16A and 16B illustrates different views of an exemplary embodimentof the portable monitoring device according to certain aspects of thepresent inventions; notably, exemplary physical specifications ordimensions (in millimeters) are outlined in connection with the top downand side views of FIG. 16A;

FIG. 16C illustrates an exemplary embodiment of the portable monitoringdevice of FIGS. 16A and 16B disposed on a base station; and

FIG. 17 illustrates, in exploded view form, an exemplary embodiment ofthe portable monitoring device of FIGS. 16A and 16B, according tocertain aspects of the present inventions; notably, the sensors (forexample, motion, altitude and/or physiological sensors) may be disposedon the main PCB.

Again, there are many inventions described and illustrated herein. Thepresent inventions are neither limited to any single aspect norembodiment thereof, nor to any combinations and/or permutations of suchaspects and/or embodiments. Each of the aspects of the presentinventions, and/or embodiments thereof, may be employed alone or incombination with one or more of the other aspects of the presentinventions and/or embodiments thereof. For the sake of brevity, many ofthose combinations and permutations are not discussed separately herein.

DETAILED DESCRIPTION

There are many inventions described and illustrated herein. In oneaspect, the present inventions are directed to portable monitoringdevices, and method of operating and controlling same, which monitor,calculate, determine and/or detect energy and/or calorie “burn” due tophysical activity of the user (for example, a human or non-human animal)and/or other activity-related metrics. The portable monitoring devicesof the present inventions include an altitude sensor, motion sensor andprocessing circuitry to calculate, assess and/or determine the calorieburn of the user and/or other activity-related metrics. (See, forexample, FIG. 1A). In one embodiment, at least a portion of the portablemonitoring device (including the one or more altitude sensors and/ormotion sensors) is affixed to the user during operation wherein thehousing of the device includes a physical size and shape thatfacilitates coupling to the user, for example, the body of the user(such as, for example, arm, wrist, angle, waist and/or foot) and allowsthe user to perform normal or typical user activities (including, forexample, exercise of all kinds and type) without hindering the user fromperforming such activities. The portable monitoring device may include amechanism (for example, a clip, strap and/or tie) that facilitatescoupling or affixing the device to the user during such normal ortypical user activities.

Briefly, during operation, the altitude sensor generates data which isrepresentative of the altitude and/or changes in altitude of the user.The motion sensor generates data which is representative of motion ofthe user. The processing circuitry, using (i) data which isrepresentative of the altitude and/or changes in altitude and (ii) datawhich is representative of the motion of the user, determines,calculates, assesses, estimates and/or detects energy and/or calorie“burn” of the user. (See, FIG. 2).

Notably, the processing circuitry may also calculate, assess, estimateand/or determine other activity-related metrics including, for example,(i) in the context of running/walking on level, substantially level, orrelatively level ground, (a) number of steps, which may be categorizedaccording to the number of steps associated with a user state, forexample, walking, jogging and/or running, (b) distance traveled and/or(c) pace, (ii) in the context of running/jogging/walking/jumping onstairs, hills or ground having a grade of greater than, for example,about 3%, (a) number of stair and/or hill steps, which may becategorized, correlated or organized/arranged according to the number ofstair and/or hill steps pertaining to, for example, the speed, paceand/or user state of the user (for example, walking, jogging and/orrunning), (b) number of flights of stairs, (c) ascent/descent distanceon stairs and/or hills, (d) pace, (e) ascent/descent on elevators and/orescalators, (f) number of calories burned or expended by walking/runningon stairs and/or hills and/or (g) quantify/compare the additionalcalories expended or burnt from stairs/hills relative to, versus or overlevel ground, (iii) in the context of swimming, number of strokes, timebetween strokes, leg kicks and similar metrics (variance of stroke time,mean stroke time, etc.), depth underwater, strokes per lap, lap time,pace and/or distance, (iv) in the context of using a bicycle,wheelchair, skateboard, skis, snowboard, ladder, etc., (a)ascent/descent distance traversed, (b) number of additional caloriesexpended, (c) time of a downward “run” or upward “climb”, (d) number ofcalories expended, (e) number of pedal rotations, (f) arm or wheelrotation, (g) the grade of the surface, (h) pushes, kicks and/or steps.This list of activities (if applicable to the particular embodiment) ismerely exemplary and is not intended to be exhaustive or limiting of theinventions to, for example, the precise forms, techniques, flow, and/orconfigurations disclosed.

The processing circuitry may be discrete or integrated logic, and/or oneor more state machines, processors (suitably programmed) and/or fieldprogrammable gate arrays (or combinations thereof); indeed, anycircuitry (for example, discrete or integrated logic, state machine(s),special or general purpose processor(s) (suitably programmed) and/orfield programmable gate array(s) (or combinations thereof)) now known orlater developed may be employed to calculate, determine, assess,estimate and/or determine the calorie burn of the user based on sensordata. In operation, the processing circuitry may perform or execute oneor more applications, routines, programs and/or data structures thatimplement particular methods, techniques, tasks or operations describedand illustrated herein. The functionality of the applications, routinesor programs may be combined or distributed. Further, the applications,routines or programs may be implemented by the processing circuitryusing any programming language whether now known or later developed,including, for example, assembly, FORTRAN, C, C++, and BASIC, whethercompiled or uncompiled code; all of which are intended to fall withinthe scope of the present invention.

With reference to FIG. 3A, in one embodiment, the motion sensor mayinclude an accelerometer and pedometer to assess the character of themotion/step and determine the number of user steps. In this embodiment,the output of the accelerometer is analyzed by the pedometer to assessthe character of the motion/step and determine the number of user steps.With reference to FIG. 3B, in addition to the accelerometer(s), or inlieu thereof, the motion sensor may include one or more gyroscopes,piezofilms, contact switches, and all combinations thereof, with orwithout the pedometer. Moreover, motion as inferred through GPS,compasses, wireless methods such as proximity sensing to a referenceposition/device, and other non-inertial sensing approaches (and theircombinations with the aforementioned inertial sensors) may also beemployed alone or in conjunction with any of the other configurationsand/or techniques. Indeed, all types of sensors and sensing techniques,whether now known or later developed, that generate data which isrepresentative of motion of the user are intended to fall within thescope of the present inventions.

With reference to FIG. 3C, in one embodiment, the altitude sensor mayinclude a pressure sensor (relative/differential or absolute), GPS,barometer, radar, ladar (i.e., laser detection and ranging), and/orinfrared proximity sensor. The portable device may also employ wirelesssignal strength, visual landmark identification, or optical proximitysensing to a known reference position/device to provide data which isrepresentative of elevation. In this regard, the altitude sensorprovides data which is representative of the altitude and/or changes inaltitude of the user. Indeed, all types of sensors and sensingtechniques, whether now known or later developed, that generate datawhich is representative of the altitude and/or changes in altitude ofthe user are intended to fall within the scope of the presentinventions.

As mentioned above, the processing circuitry employs (i) data which isrepresentative of the altitude and/or changes in altitude and (ii) datawhich is representative of the motion of the user, to determine,calculate and/or detect energy and/or calorie “burn” of the user. (See,FIG. 2). In one embodiment, the processing circuitry implementsalgorithms and/or processes data based on the flowchart of FIG. 4A. Forexample, with reference to FIGS. 4A and 4B, the processing circuitryreceives the motion sensor data and determines or calculates the calorieburn based on, for example, the character of the motion/step and thenumber of user steps. The processing circuitry, using the altitudesensor data, may adjust the calorie burn based on consideration oranalysis of the data from the altitude sensor. In this regard, theprocessing circuitry may assess or determine the type of motion thatproduces/causes the altitude or change in altitude and, in responsethereto, determine or calculate the user state—that is, activity stateof the user which temporally coincides with the motion sensor data—forexample, walking or running on relatively flat or level ground,traversing stairs, on an escalator or in an elevator, traversing a hillor the like. In response to the user state, the processing circuitry maygenerate an adjusted calorie burn. Here, the processing circuitryadjusts the calculated calorie burn with a factor that is based on theactivity state of the user as determined by the altitude sensor data.Thus, the processing circuitry correlates the (i) data which isrepresentative of the altitude and/or changes in altitude and (ii) datawhich is representative of the motion of the user, to determine orcalculate energy and/or calorie “burn” of the user.

With reference to FIGS. 4C and 4D, in one embodiment, the processingcircuitry evaluates (i) data which is representative of the altitudeand/or changes in altitude and (ii) data which is representative of themotion of the user, to identify, determine or calculate the user stateand, in response thereto, implement a user state specific algorithm ormethodology to determine or calculate energy and/or calorie “burn” ofthe user. For example, where the processing circuitry evaluates suchdata to determine that the user is traversing a hill, the processingcircuitry employs a “hill” specific algorithm to determine or calculatethe energy and/or calorie “burn” using the (i) data which isrepresentative of the altitude and/or changes in altitude and (ii) datawhich is representative of the motion of the user. In this way, thedetermination or calculation of the energy and/or calorie “burn” may bemore accurate in that the specific or dedicated algorithm may employconsiderations or features that are “unique” and/or specific to theassociated activity; and, as such, the specific or dedicated algorithmmay be tailored to the considerations or features that are “unique”and/or specific to the associated activity.

The processing circuitry may calculate, determine and/or estimatecalorie consumption, burn and/or expenditure using any technique nowknown, described herein, and/or later developed. In one exemplaryembodiment, the processing circuitry employs a calorie consumptiontechnique that estimates consumption, burn and/or expenditure forwalking, running, and lifestyle activities as follows.

Speed-Based Estimation, Calculation and/or Determination

In one embodiment, the processing circuitry may estimate calorieexpenditure and activity level based on or using, partially or entirely,the ambulatory speed of the user. For example, with reference to FIG.5A, in one embodiment, the calorie consumption, burn and/or expenditureis calculated, determined and/or estimated as a function of the speed ofthe user. Representative energy expenditure rates expressed as metabolicequivalents per minute (MET/min) at different speeds are provided inTABLE 1.

TABLE 1 Metabolic Equivalents Speed (mph) (MET/min) Running, 5.0 8.0Running, 5.2 9.0 Running, 6.0 10.0 Running, 6.7 11.0 Running, 7.0 11.5Running, 7.5 12.5 Running, 8.0 13.5 Running, 8.6 14.0 Running, 9 15.0Running, 10.0 16.0 Running, 10.9 18.0 Walking, 1.86 1.5 Walking, 2.241.9 Walking, 2.61 2.4 Walking, 2.98 3.2 Walking, 3.36 4.0 Walking, 3.735.0 Walking, 4.10 6.4

Exemplary Running and Walking Energy Expenditure (MET/Min) by Speed

In one embodiment, the speed of the user may be calculated, determinedand/or estimated as the user's step count over a time epoch multipliedby one or more step lengths of the user (which may be programmed,predetermined and/or estimated (for example, based on attributes of theuser (for example, height, weight, age, leg length, and/or gender))),which may be estimated, obtained (for example, from a look-up table ordatabase) and/or interpolated from the MET table to obtain the user'senergy expenditure. In one embodiment, step length may take one of twovalues that are indicative of a walking and a running step lengthdependent on the step frequency and/or acceleration characteristics ofthe user. In a preferred embodiment, step length may be described as alinear function of step frequency:

step length=A+B*step frequency,

-   -   where A and B are parameters that may be associated with or        calibrated to the user; notably, such parameters may be stored        in memory in the portable monitoring device.

In another embodiment, step length may be described as a function ofstep frequency and characteristics of the user acceleration:

step length=A+B*step frequency+C*variance of acceleration,

-   -   where A, B, and C are parameters that may be calibrated to the        user; notably, such parameters may be stored in memory in the        portable monitoring device.

In yet another embodiment, step length may be obtained, acquired and/ordetermined via a look-up table or database, or interpolated (e.g.,spline interpolation, neural network) between known (step frequency,step length) pairs or (step frequency, acceleration variance, steplength) triplets that have been predetermined or specified by the userand/or pre-programmed or calibrated using the device. For example, theuser may start an annotated walking or running sequence on the portablemonitoring device, then specify the distance traveled either on thedevice or through another service (e.g., www.fitbit.com), which may beemployed to calibrate or reference for the device by one or more of thefollowing:

-   -   estimating the regression coefficients (A, B) or (A, B, C),    -   calculating a walking and/or running step length, and    -   building a lookup table for (step frequency, step length) or        (step frequency, acceleration variance, step length).

In addition thereto, or in lieu thereof, the user may calibrate theportable monitoring device by attaching the device to the foot of theuser and placing the device into an annotated foot-mounted mode in whichspeed and distance are tracked and need not be entered by the user. Inthis mode, the portable monitoring device also acquires data which isrepresentative of, for example, the user's step frequency, accelerationvariance, step length. Likewise, distance may be derived from othersources such as a GPS-enabled mobile device, a software mapping utility,or other distance tracking device (e.g., Nike+) and employed todetermine the step frequency, acceleration variance, and step length ofthe user. Under certain conditions, in the absence or interruption ofGPS signal, the combination of user altitude over time, two or moregeophysical positions on a map and the times at which the user wasthere, a corresponding altitude map, and the distance over time of theuser may be used to estimate the complete route traveled by the user.

Although, in the preceding examples, the step length has beencharacterized, expressed and/or estimated as a linear function of one ormore parameters such as step frequency and variance of acceleration, thestep length and other parameter may be characterized, expressed and/orestimated to a higher order or different functional form. Accordinglysuch parameters may be expressed as polynomial functions, transcendentalfunctions, etc. as well and may include other input variables such aselevation change (as discussed in detail herein). Indeed, the functionneed not be monotonically increasing or monotonically decreasing, asimplied by the preceding illustrative linear functions. Additionally,different equations may be employed for specific activity states oroperating modes (e.g., walking, running, jumping, speed walking).

The speed value may be converted to calorie expenditure by multiplyingthe corresponding MET value by the user's BMR. BMR may be obtainedthrough any of a number of well-known equations based on height, weight,gender, age, and/or athletic ability or through designated BMRmeasurement devices. For example, a user may have a running step lengthof 57 inches and take 180 running steps during 1 min. Using the methoddescribed above, the user's speed estimate is 9.8 miles per hour, whichmay be linearly interpolated to provide a BMR value of 15.8 MET from theMET table above. Assuming the user's BMR to be 1.10 kcal/MET, thecalorie burn of the user in the preceding minute is 17.4 kcal. For theavoidance of doubt, this description is intended to be exemplary.

Speed estimation may be determined using a different time epoch or aplurality of time epochs. Multiple step lengths may be used. The METtable may be calibrated to the specific user and/or may be expressed asa function of speed in some method, such as an analytical function,discontinuous function, or otherwise. For example, in one embodiment,the relationship between speed and calories may be expressed as:

cal_speed=(A+B*speed)*time*BMR,

-   -   where speed is the speed of the user, time is the length of time        under consideration, and (A,B) are parameters that may be        calibrated to the user; notably, such parameters may be stored        in memory in the portable monitoring device.

Likewise, it is noted that an intermediate MET calculation step is notrequired in this and similar methods. Calorie expenditure may becalculated directly based on speed and one or more physiologicalparameters of the user such as age, gender, height, weight, and/orathletic ability. Speed may also be filtered over time rather thanaccepted as a “raw” measurement for a given time epoch.

FIG. 5B illustrates another embodiment in which the portable monitoringdevice may calculate, estimate and/or determine the speed of the userbased on linear and angular acceleration measurements from, forinstance, the foot. Linear acceleration may be obtained from one or moreaccelerometers. Angular acceleration may be obtained by one or moreaccelerometers, one or more gyroscopes, and/or one or more orientationsensors (e.g., compass).

Notably, the portable monitoring device may employ such techniques asthose set forth in U.S. Pat. Nos. 6,513,381 and/or 5,724,265.Alternatively, the portable monitoring device may employ the techniquesset forth in U.S. Pat. Nos. 4,578,769 and 6,018,705 wherein the deviceis mounted to the foot of the user and the speed of the user may beestimated by the time of contact of the foot on the ground.

In one embodiment, the speed of the user may be determined, calculatedand/or estimated from the signal energy from a motion sensor in thefollowing form:

speed=A*log(energy)+B

-   -   where (A,B) are parameters that may be estimated or associated        with and/or calibrated or tuned to an individual (for example,        based on the physical or motion attributes of the user);        notably, such parameters may be stored in memory in the portable        monitoring device.

In yet another embodiment, the portable monitoring device (or associateddevice) may be location aware and the travel that coincides with motiondetected by the motion sensor may be used to estimate speed. Forexample, the portable monitoring device may include or incorporate GPSto determine its location, or communicate with a GPS-enabled device toreceive data which is representative of location or change in location(absolute or relative location data). In addition thereto, or in lieuthereof, the portable monitoring device may communicate wirelessly withRF location beacons to determine position and/or change in position.Indeed, the portable monitoring device may also use signal strength tothe beacons to determine or estimate position and/or change therein.

In one embodiment, the portable monitoring device includes a camera (orcommunicates with a device that includes a camera). In this embodiment,the portable monitoring device may determine location visually byrecognizing location landmarks and/or features.

Notably, the aforementioned location sensors and methods may also beused to infer user altitude. For example, the user's location asdetermined by GPS may enable altitude estimates when combined with analtitude map. GPS itself may also provide altitude measurements.Wireless communications may also be used to determine altitude. Forexample, a RF location beacon may be programmed with its altitude or theportable monitoring device may use any of a number of well known methodsfor three-dimensional point locating such as multilateration andtrilateration.

Notably, the present inventions not intended to limit the method ormeans by which speed may be calculated, estimated and/or determined.Indeed, all forms of speed estimation, and mechanisms to implement suchtechniques, whether now known, described herein, a combination and/orfusion of the methods described herein, and/or later developed may beemployed or implemented and, as such, are intended to fall within thescope of the present inventions.

Accelerometry for Calorie Estimation, Calculation and/or Determination

In addition to speed based techniques, or in lieu thereof, the portablemonitoring device may estimate, calculate and/or determine, calorieconsumption, burn and/or expenditure using data which is representativeof the intensity of user motion—for example, as provided or determinedby one or more single axis or multi-axis accelerometers. In oneembodiment, the signals from the one or more accelerometers may befiltered using time domain or frequency domain filtering techniques toproduce a parameter indicative of the intensity of user motion, oftenreferred to as a “count”. A count may be computed as the sum of therectified filtered accelerometer output taken over a suitable timeepoch, for example, 10 sec, with or without additional processing suchas thresholding and/or saturation. The portable monitoring device maycalculate, determine and/or estimate calorie consumption, burn and/orexpenditure as a function of the current count value or a sequence ofcount values. For example, the portable monitoring device may calculate,determine and/or estimate calorie consumption, burn and/or expenditureusing one or more of the following techniques:

MET=(A+B*count)*time,

MET=(A+B*count+C*count² +D*count³+ . . . )*time, and

MET=(A*exp(B*count))*time,

-   -   which are, respectively, linear, polynomial, and exponential        relationships between counts and calorie expenditure expressed        in METs.

Notably, the preceding equations may likewise be expressed directly interms of kilocalories through the inclusion of one or more physiologicalparameters such as the user's age, gender, height, weight, and/orathletic ability (wherein such parameters may also be set to defaultvalues). A representative example is the following:

cal=((A+B*age+C*gender+D*weight+E*height+F*athleticism)*count)*time.

Indeed, all accelerometry methods, whether now known or later developed,that generate data which is representative of the calorie burn of theuser are intended to fall within the scope of the present inventions.

Heart Rate for Calorie Estimation, Calculation and/or Determination

In addition to speed based techniques and/or acceleration basedtechniques, or in lieu thereof, the portable monitoring device mayestimate, calculate and/or determine calorie consumption, burn and/orexpenditure using or based on a heart rate of the user. For example, inone embodiment, the portable monitoring device may estimate, calculateand/or determine calorie consumption, burn and/or expenditure asfollows:

cal=(A*HR+B)*time,

-   -   where HR is heart rate, time is the length of time under        consideration, and A and B are parameters that may be adjusted        or calibrated to the user based on, for example, the user's        height, weight, age, gender, and/or athletic ability; notably,        such parameters may be stored in memory in the portable        monitoring device.

In one embodiment, the portable monitoring device may estimate,calculate and/or determine calorie consumption, burn and/or expenditureusing a plurality of equations. For instance, at low or normal heartrates, it may be desirable to use one form of the above equation withparameters (A1, B1) and at higher heart rates, it may be desirable touse the above equation with parameters (A2, B2).

“Combined” Calorie Consumption Estimation, Calculation and/orDetermination

As indicated above, the portable monitoring device may estimate,calculate and/or determine calorie consumption, burn and/or expenditureusing a combination of the techniques described herein. For example,with reference to FIG. 4P, the portable monitoring device may employmotion data and heart rate data to estimate, calculate and/or determinecalorie consumption, burn and/or expenditure. In this exemplaryembodiment, under certain criteria such as low or normal heart rate inthe absence of user steps, calorie burn is calculated, determined orestimated (for example, solely) using data which is representative ofaccelerometry while under other criteria such as elevated heart rate inthe absence of user steps, calorie burn is calculated, determined orestimated (for example, solely) using data which is representative ofthe heart rate, otherwise calorie burn is calculated, determined orestimated using data which is representative heart rate, speed, andaccelerometry. An example would be the following equation:

cal_total=(p1*cal_HR+p2*cal_speed+p3*cal_accelerometry)*time,

-   -   where cal_HR is the calorie estimate derived solely from heart        rate, cal_speed is the calorie estimate derived solely from        speed, and cal_accelerometry is the calorie estimate derived        solely from accelerometry, time is the length of time under        consideration, and pi (1, 2, 3) are either fixed parameters or        dynamically adjusted parameters indicative of the certainty        and/or quality of the preceding calorie estimates.

As such, the portable monitoring device may estimate, calculate and/ordetermine calorie consumption, burn and/or expenditure based on:

cal_total=f(heart rate data, motion data, mode data, time data),

-   -   where f(•) is an arbitrary function that employs, fuses and/or        implements information from the heart rate, motion, mode, and/or        time, when such data is present and desirable or required.

In this context, information includes heart rate, heart ratevariability, respiration as obtained as a modulated signal in an opticalheart rate sensor, acceleration (raw, filtered, rectified, etc.), steps,speed, type of activity (e.g., bicycle, swimming, running, sleeping,sitting), surface slope, stairs, etc.

Notably, the aforementioned expression is intended to describe the casein which a plurality of equations are maintained and the portablemonitoring device employs or selects a suitable, correct orpredetermined equation is depending on, for example, the heart rateand/or motion as measured by the motion sensor and/or mode sensor.

The portable monitoring device may be mounted on or to differentlocations on the body of the user and/or exercise equipment and providesdifferent capabilities based on its body location. In one embodiment,the portable monitoring device may obtain or determine its body locationthrough a user input, a mode sensor that senses its body location and/orthe presence of or coupling to a mounting or attachment mechanism orsystem (for example, which provides information to the device whichindicates a mode).

In addition thereto, or in lieu thereof, the portable monitoring devicemay determine its body location automatically based on the signalsderived from its other sensors. For example, the motion as observed bythe accelerometer and/or gyroscope and/or compass and/or altimeter maybe indicative of the device being mounted or affixed to the user's foot,hip, chest, back, or on the user's bicycle hub or wheelchair wheel.Moreover, an optical heart rate sensor may provide information (forexample, supplementary information) to determine if the portablemonitoring device is in contact with the user's skin and able to observecardiac signal or otherwise in contact with a housing and/or mountingdevice. FIG. 4Q illustrates an exemplary flow to calculate, estimateand/or determine calorie burn in such a system/technique. Notably, theportable monitoring device may calculate other parameters (in additionto or in lieu of calorie burn) which are not depicted in FIG. 4Q andthat are specific to the activity/mode that the device is currently in(for example, number of steps and/or stairs, number of stair flights,elevation gain/loss from ambulatory and/or non-ambulatory locomotion,absolute elevation, activity intensity, distance traveled and/or pace,number of swim strokes and/or kicks, strokes per lap, lap time, paceand/or distance, number of pedal rotations of a bicycle, arm or wheelrotation of a wheelchair, heart rate, heart rate variability,respiration rate, stress levels, skin temperature, body temperature).Calorie burn may be determined in a multitude of ways not depicted here.For example, the calories burned during swimming may be expressed as:

cal_swim=(A+B*speed)*time,

-   -   where speed is the swimming speed of the user, time is the        length of time under consideration, and (A,B) are parameters        that may be associated with and/or calibrated or tuned to the        user; notably, such parameters may be stored in memory in the        portable monitoring device.

Where the portable monitoring device determines or is instructed thatthe user is swimming, speed may be calculated as the length of the pooldivided by the time taken to cover that distance; from which an averagedistance traveled per stroke (as observed by the motion sensor) may becalculated, estimated and/or determined and subsequent calculations orestimates of speed may be determined by the observance of swim strokesin real-time:

cal_swim=A+B*stroke,

-   -   where stroke is the stroke count of the user and (A,B) are that        may be associated with and/or calibrated or tuned to the user;        notably, such parameters may be stored in memory in the portable        monitoring device. Different or multiple equations may be        employed to account for different swimming stroke types.        Calorie burn may also be calculated roughly as a function of one        or more of the following variables: stroke type, lap count, and        swimming duration.

Where the portable monitoring device determines or is instructed thatthe user is bicycling, calorie burn may be calculated, estimated and/ordetermined by:

cal_bike=(A+B*cadence)*time,

-   -   where cadence is the number of foot pedal rotations of the user        over a time epoch, time is the length of time under        consideration, and (A,B) are that may be associated with and/or        calibrated or tuned to the user; notably, such parameters may be        stored in memory in the portable monitoring device.

Augmentation Using Altitude Data

In other embodiments, the portable monitoring device augments and/oradjusts the estimation, calculation and/or determination of calorieconsumption, burn and/or expenditure, using or based on altitude relatedinformation (for example, from an altimeter disposed on the portablemonitoring device). In these embodiments, the portable monitoring devicemay employ the speed-based calorie consumption, burn and/or expendituretechniques described herein in conjunction with altimeter or altitude(or change in altitude) related information. For example, in theMET-table based approach disclosed herein, the resulting calorie outputmay be expressed, characterized, determined, calculated and/or estimatedas:

cal=(MET(speed)*k1(ΔH-S))*time*BMR,

or cal=(MET(speed)+k2(speed,ΔH-S))*time*BMR,

or

cal=(MET(speed)*k1(ΔH-S)+k2(speed,ΔH-S))*time*BMR,

-   -   where MET(speed) is the nominal calorie output over flat land as        a function of speed, k1(ΔH-S) and k2(speed, ΔH-S) are a scaling        and offset term that are functions of ΔH-S (i.e., the change in        elevation per step) and/or speed, and time is the length of time        under consideration. k1(ΔH-S) and k2(speed, ΔH-S) are parameters        or functions that may be tuned to the user.

In another embodiment, calorie burn may be expressed or characterized as(and determined, calculated and/or estimated using) a linear function ofspeed and as a function of ΔH-S:

cal=((A+B*speed)*k1(ΔH-S)+k2(speed,ΔH-S))*time*BMR,

-   -   where (A,B) are parameters are that may be associated with        and/or calibrated or tuned to the user, and time is the length        of time under consideration; notably, such parameters may be        stored in memory in the portable monitoring device.

In the preceding equations, the terms dependent on ΔH-S may equivalentlybe written as functions of the surface slope or grade by substitutingthe distance traveled per step. Notably, this may be a more naturalexpression when speed is calculated by means other than step counting asin, for instance, foot-mounted speed and distance tracking or GPS:

cal=(MET(speed)*k1(slope)+k2(speed,slope))*time*BMR,

or

cal=((A+B*speed)*k1(slope)+k2(speed,slope))*time*BMR.

The preceding two equations are equivalent to adjusting the calorie burnestimate obtained on a level surface, adjusted with an additive and/ormultiplicative factor that is dependent on user slope and/or speed.

Furthermore, step length may be written as a function of step frequency,ΔH-S, or characteristics of the user acceleration (or combinationstherein). For instance,

step length=A+B*step frequency+C*variance of acceleration+D*ΔH-S,

-   -   where (A,B,C,D) are parameters. These parameters may be tailored        to an individual based on the calibration methods described        above or equivalent techniques.

In one embodiment, the altitude correction to energy expenditure may bean additive term calculated as:

dcal=k*speed*grade*BMR.

This equation naturally accounts for reduced energy expenditure whengoing downhill (i.e., grade<0) and increased energy expenditure whengoing uphill (i.e., grade>0). However, energy expenditure may alsoincrease when going down steep grades in excess of roughly 0.10. In suchcases, the altitude correction term may be adapted with an offset anddifferent multiplication constant k. Indeed, in a variety of scenarios,the correction term may be adapted with offsets and multiplicationconstants that are appropriate for the given activity state and/or mode(e.g., running, walking, jumping, etc.).

Certain numerical simplifications may be used to reduce the number ofcomputations performed on the portable monitoring device. For instance,by observing that the speed over a time epoch may be estimated as thenumber of user steps multiplied by the user's step length and thesurface grade may be approximated as the change in user elevationdivided by the change in user horizontal distance, the precedingadditive adjustment term may be calculated as:

dcal=k′*steps*ΔH-S*BMR.

Notably, all such mathematical manipulations of the preceding methodsthat yield identical or substantially equivalent results are consideredwithin the scope of the present inventions.

Notably, in one embodiment, if ΔH-S exceeds a predetermined threshold,the processing circuitry may determine that the user is traversingstairs, in which case, specific stair estimation algorithms may beemployed. With reference to FIG. 4R, the processing circuitry may employan embodiment in which upstairs walking and running are given specificcalorie burn algorithms based on ΔH-S. Downstairs logic may beincorporated therein. Likewise, specific equations and/or logic may beemployed for different grade hills, both upwards and downwards inaccordance with the preceding linear equations, or alternate nonlinearequations and means (e.g., lookup tables, polynomials, transcendentals,interpolations, neural nets, maximum likelihood estimates, expectedvalue estimates, etc.).

For example, in the context of running/walking on stairs, hills orground having a grade of greater than about 2-3%, the algorithm,determination or calculation of the energy and/or calorie “burn” mayemploy such considerations or factors as number of stair and/or hillsteps, surface grade, ascent/descent distance on stairs and/or hill,pace thereof, ascent/descent on elevators. Indeed, in the context ofswimming, the algorithm, determination or calculation of the energyand/or calorie “burn” may employ such considerations or factors asnumber of strokes, time between strokes and similar metrics (variance ofstroke time, mean stroke time, etc.), depth underwater, stroke type,strokes per lap, lap time, pace and/or distance. Further, in the contextof using a bicycle, wheelchair, skateboard, skis, snowboard, ladder,etc., the algorithm, determination or calculation of the energy and/orcalorie “burn” may employ such considerations or factors asascent/descent distance traversed, number of additional caloriesexpended, time of a downward “run” or upward “climb”, number of caloriesexpended, number of pedal rotations, arm or wheel rotation, the grade ofthe surface, pushes, kicks and/or steps.

As intimated above, data which is representative of the altitude and/orchanges in altitude and data which is representative of the motion ofthe user may also be used to determine and/or classify otheractivity-related metrics such as, for example, user steps, distance andpace (FIG. 4E). For example, user distance may be calculated as thenumber of user steps multiplied by the user's step length, which in turnis calculated as a function of ΔH-S and/or the change in altitude overtime (“ΔH-t”). Notably, other activity-related metrics may be determinedby the processing circuitry, including, for example, (i) in the contextof running/walking on level or substantially level ground, number ofsteps, also broken down as walking or running, distance traveled and/orpace (ii) in the context of running/walking on stairs, hills or groundhaving a grade of greater than about 3%, number of stair and/or hillsteps, which may be categorized or broken down, correlated ororganized/arranged according to, for example, the speed, pace and/oractivity state of the user (for example, as walking, jogging orrunning), number of flights of stairs, ascent/descent distance on stairsand/or hills, pace, ascent/descent on elevators and/or escalators,surface grade, and/or number of calories expended bywalking/jogging/running on stairs and/or hills as well asquantify/compare the additional calories burnt from stairs/hills overlevel ground.

In another embodiment, data from one or more physiological sensors maybe employed, alone or in combination with data of the motion sensor(s)and altitude sensor(s), to determine or assess the user state. (See, forexample, FIGS. 4F and 4O). As discussed in more detail below,physiological sensor(s) determine, sense, detect, assess and/or obtaininformation which is representative of physiological condition and/orinformation of the user (for example, blood pressure, pulse rate, bloodsugar and the waveform shape corresponding to the heart beat). Such anembodiment may, among other things, enhance the accuracy of identifyingthe user state and/or improve the confidence of the correctness/accuracyof the identified user state.

The data from the altitude, motion and physiological sensors may also beused to determine, calculate, estimate, improve and/or classify otheractivity-related metrics such as, for example, user speed and distance(FIG. 4G). Indeed, the same sensor combinations may also be used todetermine, identify and/or classify the user state in order to selectthe appropriate activity quantification algorithm—see, for example, FIG.4H wherein calorie burn corresponding to level ground, up/down hill,up/down stairs walking or running. Likewise this set or subset ofsensors may be used to estimate and/or calculate the probability of eachuser state, which may then be used to select the activity quantificationalgorithm with maximum likelihood (see FIG. 4I or 4M) or merged togetherto provide the expected value (see FIG. 4J or 4N). A number of methodsmay be devised to implement each of the embodiments shown in FIGS. 4A-4Jand 4M-4R, including but not limited to, iterative and batch algorithmsthat use ad hoc logic, statistical filtering and classificationtechniques, neural networks, k-means classifiers, and decision trees.Such conventional techniques or methods may be implemented in thepresent inventions or adaptively modified as they are used in theinvention. As such, these embodiments of the inventions are merelyexemplary and are not intended to be exhaustive or limiting of theinventions to, for example, the precise forms, techniques, flow, and/orconfigurations disclosed.

In one embodiment, the processing circuitry may evaluate the output ofthe altitude sensor to determine, calculate and/or estimate the activitystate of the user by evaluating the altitude sensor data based onalgorithms or processes based on the flowchart of FIG. 4K. Withreference to FIG. 4K, in one embodiment, the processing circuitrydetermines the type of activity by evaluating the change in altitude ofthe user on a change in height or altitude per step basis (“ΔH-S”) orthe use of an elevator by a sustained rate of height change pre timeperiod (for example, per second) (“ΔH-t”) in the absence of steps. Thechange in height or altitude per step and change in height or altitudeper second are evaluated against a plurality of thresholds and/or rangesto determine whether the user is, for example, moving (for example,running or walking) on level ground, on an escalator or in an elevator,traversing stairs and/or traversing a hill or the like. In oneembodiment, Threshold 1, Threshold 2, Threshold 3 and Threshold 4 havethe relationship Threshold 1>Threshold 2>Threshold 3>Threshold 4 whereinthe process seeks to detect and identify the causes of increases in useraltitude. In other embodiments, the flow may be modified to detect andclassify decreases or both increases and decreases in user altitude.Thus, in these embodiments, the processing circuitry employs data fromthe motion sensor to assess the user state based on data from thealtitude sensor. An exemplary implementation is illustrated in FIG. 6.

Notably, for the avoidance of doubt, the inventions are not limited toprocesses and/or algorithms implemented in accordance with the flowcharts of FIGS. 4A-4R. Such flow charts are merely exemplary. Thepresent inventions may also implement batch processing algorithms (asopposed to the real-time algorithm), the use of probabilisticclassification and estimation methods such as, for example, bayesiannetworks, Kalman filters, particle filters, unscented Kalman filters(aka sigma point Kalman filters), EM algorithm and Metropolis-Hastingsalgorithm, Gibbs sampling, Wiener filter, alpha-beta filter, orartificial neural networks. All permutations and combinations of thephysiological conditions are intended to fall within the scope of thepresent inventions.

In another embodiment, the portable monitoring device of the presentinventions includes one or more physiological sensors to further assessthe activity state of the user. For example, with reference to FIGS. 1Band 7, the physiological sensor(s) may provide data which isrepresentative of the physiological condition of the user. Theprocessing circuitry may correlate the data from the physiologicalsensor(s) with the (i) data which is representative of the altitudeand/or changes in altitude and (ii) data which is representative of themotion of the user, to determine, estimate and/or calculate energyand/or calorie “burn” of the user. For example, an apparent increase inaltitude coupled with the expected number of human steps and acorrespondingly increase in heart rate enables the processing circuitry(and techniques implemented thereby) to assess such data and moreaccurately correlate the activity to a user state—for example,distinguish stair steps from a measurement artifact.

In one embodiment, the processing circuitry may employ a decision-treebased technique/algorithm to interpret or assess changes in altitude,motion and physiological condition of the user. The decision tree basedtechnique/algorithm may employ the flow chart of FIG. 4L in which, asanother embodiment, the processing circuitry determines the type ofactivity by evaluating the change in altitude of the user on a change inheight per step basis (“ΔH-S”) or the use of an elevator by a sustainedrate of height change per temporal period (for example, seconds)(“ΔH-t”) in the absence of steps in conjunction with heart rate (“HR”).The change in height per step, change in height per second, and heartrate are evaluated against a plurality of thresholds and/or ranges todetermine whether the user is, for example, moving (for example, runningor walking) on level ground, on an escalator or in an elevator,traversing stairs and/or traversing a hill or the like. In oneembodiment, Threshold 1, Threshold 2, Threshold 3 and Threshold 4 havethe relationship Threshold 1>Threshold 2>Threshold 3>Threshold 4. Thus,in this embodiment, the processing circuitry employs data from themotion sensor to assess the user state based on data from the altitudesensor and physiological sensor. Similar techniques/algorithms mayemploy the flow of FIG. 4L and a similar flowchart based on thresholdsin relation to other certain physiological conditions including bloodpressure, pulse rate, blood sugar and the waveform shape correspondingto the heart beat.

In one embodiment, rather than making a firm “decision” based onthresholds, the processing circuitry may employ the sensor dataindividually or in combination in a statistical or ad hoc framework toprovide probabilities for each potential user state. Such techniques mayprovide a higher degree of confidence in connection with determining theuser state.

The processing circuitry may employ filtering methods and stateestimation to mitigate, address and/or alleviate noise (for example,random noise) and outliers present or inherent in the altimeter data.For example, in a preferred embodiment, several parameters of interestmay be described in state-space notation:

x(t)=[H(t)dH(t)]^(T),

-   -   where x is the state vector, H is altitude or height, and dH is        the first derivative of H with respect to time.        In this notation, dH(t) is similar or equivalent to the        parameter ΔH-t described previously. Note that these parameters        are expressed as functions of time.

In another preferred embodiment, several parameters of interest may bedescribed in an alternate state space:

x(step)=[H(step)dH(step)]^(T),

-   -   where x is the state vector, H is the altitude, and dH is the        first derivative of H now expressed as functions of step, where        the implicit relationship on time for step is omitted for        notational simplicity.        Notably, dH(step) is similar or equivalent to the parameter ΔH-S        described previously. The time-space and step-space        representations may be employed together or separately in one or        more estimation techniques. Indeed, when used separately, many        methods may be used to estimate the derivative that is not        covered in the representation. For example, when only the        time-space representation is used, the parameter dH(step) may be        estimated as:

dH _(EST)(step)=(H _(EST)(t)−H _(EST)(t−dT))/(step(t)−step(t−dT)),

-   -   where dT is a time interval, step(t) is the step count at time        t, and the EST subscript denotes an estimate.

Similarly, when the step-space representation is used, the parameterdH(t) may be estimated as:

dH _(EST)(t)=(H _(EST)(step(t))−H _(EST)(step(t)−step(t−dT)))/(t−dT),

where dT is a time interval.

Given the state vector x, many models may be employed to regularize theestimates of x in a model-based observer such as a Luenberger observer,Kalman filter, least squares estimator, recursive least squaresestimator, intermediate multiple model filter (IMM), extended Kalmanfilter, particle filter, unscented Kalman filter (aka sigma pointfilter), etc. In a time-space representation, the altitude of the usermay be described by a constant velocity model:

${{x\left( {t + {dT}} \right)} = {{{Fx}(t)} + w}},{F = \begin{bmatrix}1 & {dT} \\0 & 1\end{bmatrix}},$

where dT is a time interval and w is process noise.

In a step-space representation, the altitude of the user may bedescribed by a constant velocity model:

${{x\left( {{step} + {dStep}} \right)} = {{{Fx}({step})} + w}},{F = \begin{bmatrix}1 & {dStep} \\0 & 1\end{bmatrix}},$

where dStep=x(step(t+dT))−x(step(t)) and w is process noise.

Note that in both models, a constant altitude may be obtained by fixingdH=0. One or more constant altitude models and constant velocity modelsmay be desirable in a multiple model filter. In certain cases, it may beuseful to use a multiple model filter such as an IMM or multiplehypothesis filter in order to obtain fast estimate convergence duringtransitions that are not constant velocity. Process noise in a constantvelocity model may be tuned through historical analysis of pressuremeasurements while the user is not moving.

The model-based approach permits the prediction of future altitudemeasurements as:

x _(PRED)(t+dT)=CFx _(EST)(t),

x _(PRED)(step+dStep)=CFx _(EST)(step),

C=[1 0],

for both models.

Defining z=H+v to be a noise corrupted measurement of altitude, aresidual may be calculated as:

resid=z−x _(PRED)

which allows the statistical rejection of outliers against knownmeasurement and process noise statistics through, for instance,hypothesis testing. Other outlier rejection and step offset compensationmethods may be employed including those apparent to those skilled in theart.

By setting C=[1 0], we have implicitly assumed that the measurementsobtained are altitude measurements. However, the present inventions arenot limited to such, nor are they limited to the set of linear models asdescribed thus far. For instance, a raw measurement may be barometricpressure and it may relate to altitude by the function H=C(pressure),where C(•) is a standard, nonlinear barometric equation. In such cases,the system may be linearized to work within the framework of a linearestimator (e.g., extended Kalman filter) or a nonlinear estimator may beused (e.g., particle filter).

The processing circuitry may employ other estimation and filteringmethods. For instance, in one embodiment, H may be estimated with amedian, moving average, exponentially weight moving average, low passfilter, etc. dH(step) and dH(t) may likewise be estimated in a varietyof ways involving finite differencing with or without smoothing, etc.All such techniques are intended to fall within the scope of the presentinventions.

The processing circuitry may employ state machine logic to determine,estimate and/or calculate a change in altitude. FIGS. 8A-8F illustratesseveral embodiments of exemplary state machine logic for calculatinghuman-derived altitude. In some embodiments for calculatinghuman-derived altitude (FIGS. 8A-8C), data of the motion sensor is usedto determine if the user is currently walking/running as well as if theuser recently executed a step. This may be achieved, for instance, witha pedometer. For example, FIG. 8A depicts an embodiment where, althoughdata of the motion sensor is used to determine if the user is currentlywalking/running, the sampling of the altitude sensor (to acquire datawhich is representative of the altitude and/or change in altitude of theuser) is performed independent of the data from the motion sensor. Incontrast, FIG. 8B depicts an alternate embodiment where the samplingrate of the altitude sensor is calculated as a function of the data fromthe motion sensor. In this case, the sampling frequency F_(ALT) is afunction of the user's step frequency. FIG. 8C depicts an embodimentwhere sampling of the altitude sensor is triggered from events detectedby the motion sensor. In this exemplary embodiment, the event isdetection of a step by the user. Although not depicted in FIG. 8C,altitude measurements may be obtained following some delay after thedetection of the triggering event. This may be useful in systems where achange in measured altitude occur after some latency, as in cases wherea barometric pressure sensor is mounted in a sealed enclosure andsamples the external atmospheric pressure through a vent. Altitudesensor measurements may also be triggered by events related to the stopof motion as in, for instance, when the user stops walking for severalseconds.

Notably, in other embodiments, characteristics of the motion sensor areused to determine if the user is currently moving as well as if the userrecently executed a motion event. (See, for example, FIGS. 8D-8F). Thus,in these embodiments for calculating human-derived altitude (FIGS.8A-8C), data of the motion sensor is used to determine if the user iscurrently walking/running as well as where the user recently (forexample, within a previous predetermined amount of time) executed amotion event.

FIG. 8G provides exemplary state machine logic that may be used toaccumulate human-derived elevation gain. In this exemplary embodiment,the state machine logic employs a plurality of thresholds to indicatethe start, stop, and continuation of sustained elevation changes. Thesethresholds are denoted by T_(ij), iε {A, B, C, D}, jε{1, 2, 3, 4};however, these are intended as examples and in other embodiments of theinventions, there may be differing numbers and combinations ofthresholds. Elevation gain is accumulated when the current estimate ofelevation H_(EST) exceeds a reference H_(REF) by at least the amountgiven by the threshold, as well as when ΔH-S and ΔH-t are within certainthresholds. Appropriate settings of the thresholds may provide“hysteresis” in the accumulation of elevation gain and likewise restrictimpossible or improbable conditions. For instance, setting T_(A2)=1ft/step and T_(A3)=0 ft/step restrict user elevation gains to fallwithin those prescribed by typical staircases. A typical stair step is 6inches and a user walking up two stairs a time may cover 1 ft per step.

As mentioned above, in one embodiment, the processing circuitrycomputes, estimates and/or determines a number of stair steps traversedby the user (for example, the number of upward stair steps). Forexample, with reference to FIG. 8H, in one exemplary embodiment, whenΔH-S and ΔH-t first meet a first criteria, the processing circuitrydetermines, calculates and/or estimates an onset of the first step ofthe stair sequence. An exemplary value for the threshold T_(S2) is 5inches, which is a low riser height for a staircase. Stair steps areaccumulated thereafter until ΔH-S and ΔH-t do not meet a secondcriteria.

Notably, the processing circuitry may determine, calculate and/orestimate a first step in a stair sequence using any technique now knownor later developed. For example, the processing circuitry may employ thefollowing: One or both of the derivatives (ΔH-S and ΔH-t) may beback-propagated to their intersection with a relatively flat region ofthe measured and/or filtered altitude curve. Indeed, the first step maybe identified as the changepoint between two intersecting lines ineither the time or step-space representations (or both) and well-knownestimation techniques may be employed.

The method described here may be adapted to calculate the number ofdownward stair steps, and/or the altitude gain/loss from upward/downwardstair steps, and/or the calorie expenditure from the upward/downwardtraversal of stairs and/or the number of stair flights traversedupward/downward and/or the number of stair flight/step equivalentstraversed upward/downward (e.g., by dividing the altitude gain/loss by anominal stair flight/step height). All combinations and permutations areintended to fall within the scope of the present inventions. In oneembodiment, the portable monitoring device may be used to determine,calculate and/or estimate the step rise and/or step tread on astaircase.

In lieu of or in combination with stair steps, altitude gain, etc., theportable monitoring device may also calculate metrics (for example,motivational metrics) and/or calculate the state of avatars (forexample, a digital “pet”, a graphical representation of the user orhis/her alter ego, a game character, or physical object that glowsand/or changes physical configuration) that are partially or completelydetermined by user altitude changes. For example, the device maycalculate (and, in addition, may display to the user) “elevationpoints”, where one elevation point is representative of a change inaltitude, a stair, and/or a flight of stairs (for example, one elevationpoint is equal to approximately ten feet, or one flight of stairs). Auser may then be motivated to increase an elevation point score or totalby, for example, traversing more stairs, flights of stairs and/or hills.Moreover, the device may also maintain the state of a virtual avatar,for example, a flower, whose growth and/or health is related to useraltitude changes, or a building, whose size and/or growth is related touser altitude changes, or an entity that morphs between states that areindicative of increased or decreased elevation gains such as a staircase, ladder, hill, or mountain, or specific landmarks like the EiffelTower, Mt. Everest, and the Moon. Indeed, all games and/or avatars thatare controlled in part or wholly by changes in altitude sensor data areintended as embodiments of the present inventions.

In other aspects of the present inventions, the altitude changes may becombined, integrated and/or fused with other information such as userspeed, step frequency, surface grade, stair steps, calorie burn, energyexpenditure, heart rate, etc. to obtain more general “activity points”,where, for example, activity points may be described as:

AP=k1*MET+k2*ΔH+k3*(HR−HR ₀),

-   -   where k1, k2, and k3 are parameters, MET are metabolic        equivalent units expended, HR is the user's heart rate and HR₀        is a nominal at-rest heart rate.        This equation is provided merely for illustration. Indeed, all        relationships that describe activity points and/or “grades”        and/or activity metrics that are not inherently physical        quantities (e.g., calorie burn) and/or avatar states as an        integrated, combined and/or fused output from one or more of        user motion data, user physiological data, user elevation data,        and/or user location data are simply embodiments of the present        inventions. Likewise, all relationships that describe elevation        points, and/or “grades” and/or metrics that are not inherently        physical quantities (e.g., elevation gain) and/or avatar states        as an output from either altitude data alone or in combination        with user motion data, user physiological data, and/or user        location data are simply embodiments of the present inventions.        In some embodiments, the motivational metrics and/or avatars may        be computed and/or displayed on the portable monitoring device.        In other embodiments, the motivational metrics and/or avatars        may be computed and/or displayed on other media (e.g.,        www.fitbit.com) using data from the portable monitoring device.        In yet other embodiments, the motivational metrics and/or        avatars may be computed and/or displayed on both the portable        monitoring device and other media.

Notably, in one embodiment, the processing circuitry may adjust certainthresholds (for example, thresholds employed in conjunction with thetechniques described herein) dynamically according to a variety ofparameters to mitigate noise and drift from appearing in the outputaccumulated elevation gain. For example, such parameters may be based onsurface conditions, weather, temperature, humidity, user motion, andphysiological data from the user. Examples include the surface grade,user speed, user step frequency, user energy expenditure rate, useractivity state (e.g., walking versus running versus jumping), ΔH-S,ΔH-t, weather conditions (e.g., incoming storm, wind, rain), the rate ofchange in barometric pressure while the user is not moving, the rate ofchange of temperature change, variation in the barometric pressuresignal and/or altitude measurement (e.g., random noise), motion sensordata energy, motion sensor data variation, and/or heart rate.

In an exemplary embodiment, the thresholds required to accumulate anelevation gain follow a decreasing function with respect to surfacegrade, such that shallower grades require larger altitude gains andsteeper grades permit smaller altitude gains. Furthermore in thisregard, different functions may be used for walking and running suchthat shallower grades have lower thresholds for running than they do incomparison to walking. This is motivated by the fact that humansselectively perceive surface grades according to how fast they move onthe surface. That is, shallower grades are easier to detect when theuser is running rather than walking. Similar effects may be achieved bysetting the thresholds as a function of surface grade and user speed orsurface grade and user step frequency. Indeed, all possible combinationsof the previously mentioned parameters may be used. Note that certainsurface grades may be rejected from accumulation by setting theirrespective thresholds to infinity. In one embodiment, elevation gainsand/or losses are only permitted for grades in excess of ±2%. Thethresholds may also be adapted according to the drift and/or noisepresent on the altitude sensor while the user is not moving. In thisway, the algorithm may adjust for changing weather conditions asobserved by a barometric pressure sensor.

Surface grade may be expressed as the elevation change over a horizontaldistance:

g=ΔH/d.

When d is not directly measured (as in the case of GPS tracking), it maybe calculated as:

d=sqrt(d _(s) ² +ΔH ²),

where d_(s) is the distance traveled overland by the user.

For typical walking and running surface grades, it is sufficient toapproximate d=d_(s). Other numerical approximations exist and areapparent to one skilled in the art. The overland distance may bemeasured by any of a variety of methods, some of which were describedabove. They include use of a pedometer function or foot-mounted distancetracking. In other embodiments, the inventions may have functionalitythat determines the elevation change and/or slope between two pointsthrough, for instance, the use of GPS with an altimeter.

Notably, FIGS. 8A-8H are illustrative and are not intended to limit theimplementation of the present inventions. For the avoidance of doubt,the thresholds depicted in the figures may be implemented on themeasurements and/or estimates of altitude. The threshold logic may alsobe implemented over a sequence of points or over a time interval orboth. In certain circumstances, it may not be useful or desirable toinclude all of the threshold parameters described. For instance, it maynot always be useful to include thresholds against ΔH-S or ΔH-t. Thethresholds may also be supplanted with probabilistic functions evaluatedjointly or conditionally over (ΔH, ΔH-S, ΔH-t) or individually over someor all of the same parameters. The portable monitoring device may alsoemploy a variety of timers and event counters to block certaincalculation steps when, for instance, significant jumps in elevation areseen, noise increases, or outliers are encountered. Timers and/orcounters may also be employed to reset the state of the algorithm (forexample, state=0) and/or filter when a downward or upward accumulationevent has not occurred within a certain time interval or number ofiterations. The reference altitude H_(REF) may also be refined over timeor reinitialized according to certain criteria through a variety offiltering and estimation techniques. The methods described herein may beadapted to calculate elevation loss, or to calculate elevationgains/losses distinguished by surface grade and/or stair conditions.

The sampling rate of the sensors of the portable monitoring device maybe predetermined or fixed. In one embodiment, the sampling rate isprogrammable or controllable. For example, in one embodiment, theportable monitoring device may control and/or determine the samplingrate of the sensors based on considerations of electrical powerconsumption and the rate thereof. In this regard, the portablemonitoring device may employ electrical power saving circuitry and/ortechniques to control and/or determine the sampling rate of the sensors.

In one embodiment, the sampling frequency of the altitude sensor(F_(ALT)) may be controlled, determined and/or calculated based on datafrom the motion sensor. For example, with reference to FIG. 9A, in oneembodiment, if the motion of the user does not exceed a threshold withina time period (for example, a predetermined or programmable timeperiod), the altitude sensor may be placed into a mode that does notsample (for example, a low power mode), or F_(ALT) may be reduced ordecreased. Subsequently, if the motion exceeds a threshold, for example,for a predetermined or minimum time duration, F_(ALT) may be increased.

Notably, “motion” in this context is meant generally and includesfeatures derived from raw motion sensor data. Examples would be thesignal energy, variance, range, etc. The mapping of motion to F_(ALT)may be a continuous function or discrete settings. In the case of one ormore discrete settings (e.g., sampling modes), the features used totransition between modes are not necessarily the same, nor are thethresholds and other parameters dictating the transitions.

FIGS. 9B and 9C depict other exemplary embodiments in which the samplingmode or frequency of the altitude sensor is determined by the output ofa pedometer.

Here again, the portable monitoring device senses the motion of the userand, based thereon, controls and/or determines the sampling rate of thealtitude sensor. In this way, the portable monitoring device manages orcontrols the electrical power consumption, and the rate thereof.

FIG. 9D illustrates another embodiment where altitude sensor readingsare triggered from step events detected by a pedometer, or a maximumtime T between samples (whichever occurs first). Although not depictedin FIG. 9D, altitude sensor readings may be scheduled to occur after adelay from the step event. The step-space representation and modelsdescribed above are suited to this type of sampling. Setting T=∞ reducesto the case where altitude readings are only generated throughtriggering from a pedometer.

In another embodiment, the altitude sensor may be read or its samplingmode/frequency may be controlled or set according to peaks and/or othermorphological features derived from the motion sensor. For example, thesampling rate of the altimeter may be set higher and/or its measurementsettings may be adjusted (e.g., to a finer setting) if the motion sensorprovides a signal that is indicative of the user losing balance and/orabout to fall or falling (e.g., free fall). Similarly, the samplingsettings may be adjusted to capture fast transient elevation changesthat may be experienced during jumping with one's legs or by other means(e.g., skateboard, skis and snowboard, pole vaulting, and the like—thesettings being determined by data from the motion sensor that isindicative of the activity).

The aforementioned discussions in connection with FIGS. 8A-8H and 9A-9Dmay be implemented in conjunction with any of technique to calculatecalorie burn, including the techniques described and illustrated herein.All permutations and combinations of (i) calculating elevation oraltitude change and (ii) altitude sampling techniques, and (iii)techniques to calculate calorie burn are intended to fall within thescope of the present inventions. Moreover, as stated above, theinventions are not limited to processes and/or algorithms implemented inaccordance with the flow charts of FIG. 8A-8H and 9A-9D. Such flowcharts are merely exemplary. The present inventions may also implementbatch processing algorithms (as opposed to the real-time algorithm), theuse of probabilistic classification and estimation methods such as, forexample, bayesian networks, Kalman filters, EM algorithm andMetropolis-Hastings algorithm, or artificial neural networks.

The portable monitoring device of the present inventions may alsoinclude a user interface to facilitate communication with the user.(See, for example, FIG. 1C) The user interface may include one or moredisplays, one or more of a speaker, microphone, vibramotor, and/or aninput mechanism. (See, for example, FIGS. 10A-10F). Indeed, any mannerof or mechanism for outputting and/or inputting of data and/or commandsare intended to fall within the scope of the present inventions.

In one embodiment, the portable monitoring device includes one or moremode sensors to input, detect and/or determine a mode of movement by theuser. (See, for example, FIG. 1D). For example, the user may input,detect and/or determine that the user is in a wheelchair, on a ladder,skate board, skis, snowboard and/or a bicycle. In response thereto, theprocessing circuitry may correlate and/or employ the data from the modesensor(s) with the (i) data which is representative of the altitudeand/or changes in altitude and (ii) data which is representative of themotion of the user, to determine, estimate, and/or calculate energyand/or calorie “burn” of the user. For example, where the user is on abicycle, the processing circuitry may determine or calculate energyand/or calorie “burn” of the user using the (i) data which isrepresentative of the altitude and/or changes in altitude and (ii) datawhich is representative of the motion of the user. Notably, the modesensor may be responsive to a user input or detected mode of movement.

In one embodiment, FIG. 11A depicts the processing flow for the use ofthe mode sensor in selecting the appropriate algorithm for determiningcalorie “burn” of the user. Similarly, FIG. 11B depicts the processingflow for the use of the mode sensor in selecting the appropriateactivity-tracking algorithms for the user based on the mode of motion ormovement.

There are many mechanisms and techniques by which the mode sensor(s) maybe implemented. One embodiment employs buttons and a feedback mechanismsuch as a graphical display, flashing lights, haptic device,piezoelectric buzzer, vibramotor, and/or speaker (all, for example,elements of the user interface) to navigate a menu system to selectdifferent modes. Another embodiment uses one or more of the motionsensors to recognize user gestures to select and deselect certain modes.In yet another embodiment, the portable monitoring device may include auser interface having an input device (for example, one or more buttons)and/or sensors that mate with specialized housings for each mode. Forexample, placing the device onto a designated wheelchair mounting devicecould push a button on the device to select a wheelchair mode. Inanother embodiment, device may be placed near a designated RF beaconthat is affixed to a bicycle spoke, in which case the device wouldexecute bicycle mode functionality. Other implementations may use, forexample, RFIDs, magnetic sensors, LEDs and photodetectors, piezoelectricstrip/material and/or strain gauges, to detect the presence of thespecialized mounting apparatus. In yet another embodiment, the motionsensor(s), altitude sensor(s), and physiological sensor(s) are employedto recognize user activity and automatically select the mode.

Notably, the mode may be selected and/or determined from a plurality ofpre-programmed or predetermined modes (for example, during manufacture).Such pre-programmed or predetermined modes may be stored in memory inthe processing circuitry of the device. In addition thereto, or in lieuthereof, the modes may be user defined (after manufacture—for example,in situ or during operation) and programmed into or onto the device bythe user at a later time and corresponding activity quantificationalgorithms may be adaptively “trained” or “taught” by the user.

In yet another embodiment, the portable monitoring device includes themotion sensor, altitude sensor, physiological sensor and mode sensor.Indeed, all permutations and combinations of sensors, whether inconjunction with a user interface or not, may be employed or implementedin a portable monitoring device according to the present inventions. Allsuch combinations and permutations are intended to fall with in thescope of the present inventions.

The portable monitoring device may include transmitter circuitry tocommunicate energy and/or calorie “burn” of the user to, for example, anexternal user interface, the Internet, social or media site (forexample, Fitbit or Facebook) and/or computing system. (See, for example,FIG. 1F). The portable monitoring device may also output raw orpseudo-raw sensor data as well as a correlation thereof (see, forexample, FIG. 6). Indeed, the portable monitoring device may output theother activity-related metrics, including, for example, (i) in thecontext of running/walking on level, substantially level, or relativelylevel ground, (a) number of steps, which may be categorized according tothe number of steps associated with a user state, for example, walking,jogging and/or running, (b) distance traveled and/or (c) pace, (ii) inthe context of running/walking on stairs, hills or ground having a gradeof greater than, for example, about 3%, (a) number of stair and/or hillsteps, which may be categorized, correlated or organized/arrangedaccording to, for example, the speed, pace and/or activity state of theuser (for example, the number of stair and/or hill steps pertaining towalking, jogging and/or running), (b) number of flights of stairs, (c)ascent/descent distance on stairs and/or hills, (d) pace, (e)ascent/descent on elevators, (f) number of calories expended bywalking/jogging/running on stairs and/or hills and/or (g)quantify/compare the additional calories expended or burnt fromstairs/hills relative to, versus or over level ground, (iii) in thecontext of swimming, number of strokes, time between strokes, leg kicksand similar metrics (variance of stroke time, mean stroke time, etc.),depth underwater, strokes per lap, lap time, pace and/or distance, (iv)in the context of using a bicycle, wheelchair, skateboard, skis,snowboard, ladder, etc., (a) ascent/descent distance traversed, (b)number of additional calories expended, (c) time of a downward “run” orupward “climb”, (d) number of calories expended, (e) number of pedalrotations, (f) arm or wheel rotation, (g) the grade of the surface, (h)pushes, kicks and/or steps. This list of activities (if applicable tothe particular embodiment) is merely exemplary and is not intended to beexhaustive or limiting of the inventions to, for example, the preciseforms, techniques, flow, and/or configurations disclosed.

The portable monitoring device of the present inventions may includecommunication circuitry which implements or employs any form ofcommunications (for example, wireless, optical, or wired) and/orprotocol (for example, standard or proprietary) now known or laterdeveloped, all forms of communications and protocols are intended tofall within the scope of the present inventions (for example, Bluetooth,ANT, WLAN, Wi-Fi, power-line networking, all types and forms of Internetbased communications, and/or SMS); all forms of communications andprotocols are intended to fall within the scope of the presentinventions.

The portable monitoring device may include receiver circuitry to morefully communicate with the user and/or external circuitry. (See, forexample, FIG. 1G). For example, the portable monitoring device mayreceive external data or commands regarding exercise time, energy useand/or calorie “burn”, and milestones, for example, from the internet,social or media site (for example, Fitbit or Facebook) and/or computingsystem; all forms of receiver circuitry and receiving protocols areintended to fall within the scope of the present inventions.

Again, all permutations and combinations of sensors, user interface,transmitter circuitry and receiver circuitry, may be employed orimplemented in a portable monitoring device according to the presentinventions. (See, for example, FIGS. 1A-1X). All such combinations andpermutations are intended to fall with in the scope of the presentinventions.

As such, the portable monitoring device of the present inventions mayinterface or communicate via any connectivity and protocol (for example,wired, wireless, electrical and/or optical and/or all types and forms ofUSB and/or removable memory). All communication mechanisms, techniquesand architectures are intended to fall within the scope of the presentinventions. Thus, the portable monitoring device may employ wired and/orwireless transmitter circuitry to communicate energy and/or calorie“burn” of the user to, for example, an external user interface, theinternet, social or media site (for example, Fitbit or Facebook) and/orcomputing system. (See, for example, FIG. 1F). As noted above, theportable monitoring device may also output raw or pseudo-raw sensor dataas well as a correlation thereof (see, for example, FIG. 6). Indeed, theportable monitoring device may be communicate energy and/or calorie“burn” or expenditure of the user (or such raw or pseudo-raw sensordata), for example, via transmitter circuitry, removable memory,wireless and/or wired (for example, electrical or optical)communication.

For example, in one embodiment, the portable monitoring device may beplaced into a data transfer mode (for example, via engagement with adock station, user input/instruction and/or proximity to base device) inwhich the display and/or suitable visual elements are used to transmitdata, for example, to a base device (for example, a mobile phone,computer and/or internet portal device (for example, a router)). Thebase device may include one or more visual monitoring devices. In oneembodiment, for example, the portable monitoring device may transmitdata by switching on/off LCD segments, switching on/off individual orclusters of display pixels, and/or modulating the intensity and/or colorof display pixels in the display of the user interface while base device(for example, mobile phone) monitors the display sequence with a cameraand/or video camera. The data may be transmitted in any format now knownor later developed including a suitable human readable format (forexample, numbers and words), a binary sequence of bits (for example, barcode), or otherwise.

In another embodiment, the portable monitoring device includes orprovides for bidirectional communication of the second portablemonitoring device and/or a base device. In this embodiment, the seconddevice controls a light source (e.g., camera phone flash, differentcolors on screen, pixels on/off) and the portable monitoring deviceanalyzes and/or monitors the visual sequence with one or more visualsensors. This method of data transfer and communication may remove theneed for additional wireless and/or wired hardware for transmitting datafrom the invention to another device, which itself may transfer the datato another service (e.g., www.fitbit.com).

As discussed herein, the display of the user interface may be theprimary mechanism of displaying information to the user. That displaymay be placed into a mode to facilitate or execute an optical transfercommunication. In other embodiments, the portable monitoring device mayhave specific visual sources that are only intended for data transfer,as well as the combination of both the primary user display and a datatransfer display.

Notably, because alignment and placement of the portable monitoringdevice with a second device are important to the transfer process,either or both of the devices may have on-screen guides or instructionsto aid alignment and placement. In one embodiment, the second device mayshow a template overlaid in its video display to help the user place theinvention correctly relative to the second device. The portablemonitoring device may have visual landmarks (e.g., borders, buttons,colors, and/or display elements) that enable the second device tovisually track certain aspects of the portable monitoring device duringdata transfer. Visual tracking may also provide the user with alignmentcues to improve placement (e.g., arrows). These same strategies may beemployed by the portable monitoring device in cases of bidirectionalcommunication.

The portable monitoring device may be equipped with one or morevibramotors, buzzers, and/or speakers with which to alert the user. Forinstance, the device may buzz or emit a sound to encourage the user towalk or move after observing a sedentary period of 30 minutes or more.The device may buzz or emit a sound in order to notify the user that thebattery level is low. The device may buzz or emit a sound to act as atime-based alarm. Indeed, all manner of audible and/or haptic alerts areconsidered to be within the scope of the present inventions.

There are many inventions described and illustrated herein. Whilecertain embodiments, features, attributes and advantages of theinventions have been described and illustrated, it should be understoodthat many others, as well as different and/or similar embodiments,features, attributes and advantages of the present inventions, areapparent from the description and illustrations. As such, the aboveembodiments of the inventions are merely exemplary. They are notintended to be exhaustive or to limit the inventions to the preciseforms, techniques, materials and/or configurations disclosed. Manymodifications and variations are possible in light of this disclosure.It is to be understood that other embodiments may be utilized andoperational changes may be made without departing from the scope of thepresent inventions. As such, the scope of the inventions is not limitedsolely to the description above because the description of the aboveembodiments has been presented for the purposes of illustration anddescription.

For example, in one embodiment, the portable monitoring device of thepresent invention includes an altitude sensor and motion sensor (and incertain embodiments other sensors such as one or more physiologicalsensors and/or one or more mode sensors). In this embodiment, theportable monitoring device, however, may not include processingcircuitry to monitor, calculate, determine and/or detect energy and/orcalorie “burn” due to physical activity of the user (for example, ahuman or non-human animal). In this embodiment, some or all of themonitoring, calculating, determining and/or detecting may be implemented“off-device” or external to the portable monitoring device. Here, theportable monitoring device may store and/or communicate (i) data whichis representative of the altitude and/or changes in altitude of the userand/or (ii) data which is representative of motion of the user toexternal processing circuitry wherein such external processing circuitrymay monitor, calculate, determine and/or detect energy and/or calorie“burn” due to physical activity of the user. (See, FIG. 12A). Suchexternal circuitry may implement the calculation processes andtechniques in near real-time or after-the-fact. The data which isrepresentative of the (i) altitude and/or changes in altitude of theuser and/or (ii) motion of the user may be communicated to such externalprocessing circuitry, for example, via transmitter circuitry (see FIG.12A), removable memory, electrical or optical communication (forexample, hardwired communications via USB). Importantly, such anarchitecture/embodiment is intended to fall within the scope of thepresent inventions.

Moreover, the portable monitoring device of this embodiment (i.e.,external processing circuitry) may include all permutations andcombinations of sensors (for example, one or more physiologicalsensor(s) and/or mode sensor(s). For example, the portable monitoringdevice of the present inventions may include one or more altitudesensors (see, for example, FIGS. 1A-1L); in other embodiments, theportable monitoring device does not include one or more an altitudesensors (see, for example, FIGS. 1M-1X).

Notably, in one embodiment, the processing circuitry to monitor,calculate, determine and/or detect energy and/or calorie “burn” due tophysical activity of the user may be distributed between residentcircuitry and external circuitry. (See, FIG. 12B). In this embodiment,circuitry disposed in the portable monitoring device may implementcertain processes and algorithms and the external processing circuitrymay implement other processes and algorithms wherein, the circuitry, incombination, monitors, calculates, determines and/or detects energyand/or calorie “burn” due to physical activity of the user.

In another embodiment, the exemplary portable monitoring devices mayemploy a MEMS altitude sensor. In this regard, the altitude sensorincludes a MEMS pressure sensing structure to generate data which isrepresentative of the altitude of the structure. For example, withreference to FIG. 13, in one embodiment, the MEMS pressure sensingstructure includes a multi-diaphragm structure wherein a first diaphragm1 is more fully exposed to changes in the ambient environment relativeto a second diaphragm which is less exposed to changes in the ambientenvironment due to a plurality of micro-pores 3. The diaphragms 1 and 2form a portion of a sealed chamber (for example, vacuum-sealed chamber).In one embodiment, the MEMS pressure sensing structure includes apressure sensing element 5 to sense, sample, determine and/or obtainchanges in pressure. Here, first diaphragm 1 is responsive to theenvironment with low pneumatic impedance so that there is effectively nolatency. The second diaphragm is responsive to the environment throughmicro-pores that “delay” the rate of change of the pressure. Thisdifference may deflect the bidirectional pressure sensing element 5toward the side with lower pressure.

The sensor structure depicted in FIG. 13 is just one embodiment of oneaspect of the inventions. The chambers on either side of thebi-directional sensing element need not be equal in size and either orboth sealing diaphragms 1 and 2 may be omitted in some embodiments. Forinstance, the element producing the pneumatic impedance that delayspressure changes may effectively seal the chamber from contaminantsbecause the element is a continuous polymer film.

In operation, the deflection in pressure sensing element 5 providesinformation which is representative of the change in altitude (due tochanges in pressure). That is, the micro-pores “delay” the rate ofpressure change caused by the second diaphragm in response to smallchanges in altitude relative to the first diaphragm (which has a lowpneumatic impedance path to ambient). The deflection of in pressuresensing element 5 may be measured via changes in stress or strain inpressure sensing element 5. In addition thereto, or in lieu thereof, thedeflection may be measured by changes in capacitance or voltages ofsensing plates (not illustrated).

Pressure equalization in a single-pore system can be expressedanalytically for a simplified system. Assuming the inner chamber hasvolume V and is subject to a pressure difference of Δp which is muchless than the absolute pressure p (i.e., Δp<<p) and that the innerchamber is exposed to the external environment through a hole of radiusr and that the system has low Knudsen number, the mean velocity of gasflow at the orifice is

${u = {{- \frac{r}{3\; \pi \; \eta}}\Delta \; p}},$

where η is the viscosity of the gas (Roscoe 1949, Yu, et al. 1988).

The rate of change of the mass of gas in the chamber is

{dot over (m)}=ρπr ² u,

where ρ is the density of the gas.

Finally, assuming the gas to be ideal and the system to be isovolumetricand isothermal, it can be shown that the rate of change of pressure inthe chamber p_(c) is

${{\overset{.}{p}}_{c} = {\left( \frac{\rho \; {RT}}{3\; \eta \; M} \right)\left( \frac{r^{3}}{V} \right)\Delta \; p}},$

where R is the universal gas constant, T is the temperature, and M isthe molar mass of the gas.

As such, it can then be shown that the time constant for the system τ is

$\tau = {\left( \frac{3\; \eta \; M}{\rho \; {RT}} \right){\left( \frac{V}{r^{3}} \right).}}$

For illustration, operating at standard temperature and pressure,representative numbers for air are M=0.0289 kg mol⁻¹, η=1.8369×10⁻⁵ Nsm⁻², ρ=1.2041 kg m⁻³. Assuming the chamber to be a 1 mm cube (V=10⁻⁹m³), a 1 sec time constant (i.e., τ=1 sec) can be achieved with a holeof size r=0.816 μm. The Knudsen number of the system is 0.04<<1 so thepreceding assumption of small Knudsen number is appropriate.

In the preceding analysis, the effects of a finite length to the orifice(so that it acts as a channel) and other parasitic effects such assurface tension are omitted. These effects increase the pneumaticresistance of the pore and enable it to be sized larger for a given timeconstant τ. It is assumed that one skilled in the art can derive asimilar expression or use simulation to account for these effects. Theatmospheric permeability of thin polymeric membranes that can also beapplied across macro-sized holes in the relatively deep silicon walls ofa MEMS produced pressure chamber, or cover arrays of micro-machinedsurface channels leading to such a chamber can be employed and are wellunderstood. For instance, the O₂ permeability of various common polymerswas documented by Robb (1968) to span more than 5 orders of magnitude,making selection of a suitable thickness of gas permeable membrane morea choice of manufacturing convenience then of limited alternativechemistries.

Similarly, gas porous silicon membranes capping a chamber or channelsleading to a chamber can be made by MEMS processes with bulk etching &processing techniques to produce predictable gas permeabilitycharacteristics. For instance, Galambos, et al. (1999) demonstrated thatsilicon nitride (Si₃N₄) could itself be etched to produce a gaspermeable filter in a micromachined channel. More generally, Wu (2004)described the properties of porous polycystalline membranes etched onSi₃N₄ using MEMS technology to cover cavities. MEMS processingtechniques may produce a membrane structure with stochasticallypredictable micro cracks or pores that facilitate atmospheric gas flow,without the need for precisely machined orifices.

Importantly, the present inventions are neither limited to any singleaspect nor embodiment, nor to any combinations and/or permutations ofsuch aspects and/or embodiments. Moreover, each of the aspects of thepresent inventions, and/or embodiments thereof, may be employed alone orin combination with one or more of the other aspects and/or embodimentsthereof. For example, while many of the inventions have been describedin connection with a portable monitoring device including one or morealtitude sensors (see, for example, FIGS. 1A-1L), many of the inventionsmay be implemented in connection with a portable monitoring device whichdoes not include one or more an altitude sensors (see, for example,FIGS. 1M-1X). For the sake of brevity, many of those permutations andcombinations will not be discussed and/or illustrated separately herein.

Notably, although exemplary embodiments and/or processes have beendescribed above, the inventions described and/or illustrated herein mayalso be implemented in conjunction with other activity metricdetermination techniques. As such, the inventions are not limited toprocesses and/or algorithms implemented in accordance with the flowcharts of FIGS. 4A-4R; rather, such flowcharts are merely exemplary.

Further, the portable monitoring device may communicate with externalcircuitry using the transmitter circuitry (see, for example, FIGS. 1F,1H-1L, 1R and 1T-1X), receiver circuitry (see, for example, FIGS. 1G,1J-1L, 1S, and 1V-1X), removable memory, electrical or opticalcommunication or connector (for example, hardwired communications viaUSB).

As mentioned above, the portable monitoring device may store and/ortransmit the raw data or pseudo-raw (i.e., processed) data from on ormore (or all) of the sensor(s). For example, in the context of themotion sensor, the portable monitoring device may store and/or transmitdata which is representative of acceleration, angular rate, locationand/or compass bearing. In the context of the altitude sensor, in oneembodiment, the portable monitoring device may store and/or transmitdata which is representative of pressure, altitude, time of flightand/or radar cross section. Further, in the context of the physiologicalsensor(s) and naturally-derived metrics, the portable monitoring devicemay store and/or transmit data which is representative of heart waveform(for example, ECG trace), heart rate, blood sugar, blood pressure and/orEEG. In addition, in one embodiment, the portable monitoring device maystore and/or transmit data provided by the mode sensor(s) including, forexample, bicycling, swimming, skateboard or wheel chair.

Notably, the data which is stored and/or transmitted may be filteredversions of the aforementioned, for example, filtered using passiveelements (for example, RC networks) and/or active elements (for example,active electronics), frequency-domain methods (Butterworth filter, etc),statistical methods (Kalman filter, etc), time-series analysis (ARXmodels, etc) and/or wavelets.

The raw or pseudo-raw (for example, filtered versions) of theaforementioned data may be stored and/or transmitted in time epochs thatdiffer from the original (e.g., 1 Hz instead of 100 Hz) or in summaryversions (e.g., mean, variance, integral, power, coefficient ofvariation, etc.) and may include signal quantities that are derivedtypically for use in a classification algorithm and other downstreamcalculations. In addition, the raw or pseudo-raw (for example, filteredversions) of the aforementioned data may be stored and/or transmitted incompressed or uncompressed formats. Such data may also be stored and/ortransmitted in a matched to a value format that, for example, capturesthe approximate or exact value of the data (e.g., look-up table, rangeslike “small”, “medium” and “large”).

The data and parameters derived by the portable monitoring device may betransferred, displayed, and/or modified remotely as in, for example, acomputer program or website such as www.fitbit.com. Such content mayfurthermore be modified by the remote application and transferred backto the device for storage and display. For example, the user may adjustinformation regarding one or more physiological parameters that effectmetabolism, which in turn are used to correct calorie burn estimates onthe portable monitoring device. Likewise, the user may adjustinformation regarding height, step length, the intensity of a workout,the type of activity over a particular time duration (e.g., walking,running, weight lifting, driving or riding in an automobile, etc.) andthis information may be used to adjust estimates of calorie burn,distance traveled, speed, avatar state, and other activity-relatedmetrics stored and/or displayed on the portable monitoring device.

Data and derived parameters from one or more of the present inventionsand/or one or more other devices may be stored, displayed, and/ormodified remotely as in, for example, a computer program or website suchas www.fitbit.com. The devices may generate their data independently,operate dependently upon one another (e.g., as accessories to oneanother), or both. The remote application may combine the data orgenerate new data which is then displayed in the remote applicationand/or other remote applications and/or the portable monitoring deviceof the present inventions and/or other devices. The remote applicationmay also overlay the data to the user to present a holistic view of thedata streams obtained from multiple devices. For example, two devices(called “Device A” and “Device B” for simplicity here which arerespectively a pedometer and heart rate monitor) may generate datastreams representative of user activity and/or physiological informationwhich is transferred to www.fitbit.com. The user may create a datastream of manually or automatically annotated events on Device B, whichare subsequently used by www.fitbit.com to override or modify the datafrom Device A during the same periods of time as the annotated events.The annotated events may likewise be presented in a manner that attractsuser attention preferentially to the data of Device B. Notably, the datathat is overridden or modified may be transferred back to Devices A andB for storage and/or display to the user.

In addition, the portable monitoring device may also facilitate socialinteraction features. In one embodiment, the portable monitoring devicemay contain user identification “credentials” (for example, stored inmemory) that communicate with second or base devices to link a pluralityof users (for example, two users) in a social network (for example, viathe internet or device-to-device). The second device may or may not be aportable monitoring device according to one or more of the embodimentsof the present inventions. As an example, two people may link theirdevices to become “friends” on www.facebook.com, www.fitbit.com,www.linkedin.com, and/or other social networks. The “friend” status maybe may be passed to the internet directly from the device or routedthrough intermediate devices (e.g., Fitbit Tracker to mobile phone tointernet). Linking of devices may be achieved in a variety of well-knownways for pairing two devices. In one embodiment, the two devices may belinked through a bump gesture or physical tapping (e.g., contact) of thetwo devices that initiates wireless communication between the two. Acombination of motion sensing (e.g., accelerometer and/or gyro), and/ormagnetic signature sensing, and/or wireless (e.g., NFC, RFID, RFproximity sensing) may be employed.

In other embodiments, the portable monitoring device may provide usercontact information such as email addresses, telephone numbers and/or auser name, which may be transmitted when the devices are linked. Thedevice may be configured to transmit only specific pieces of contactinformation.

In other embodiments, the portable monitoring device may monitor,catalog and/or track the number, duration, frequency, and quality ofsocial interactions between two or more people in a social network. Asan example, the portable monitoring device may use wireless proximitydetection between portable monitoring devices to determine, monitor,catalog and/or track episodes in which the devices were in the sameproximity, for example, to determine, monitor, catalog and/or track theamount of time two users spent time with one another. Summary metricslike the duration of interaction, frequency of meetings, physicaldistance, etc. are exemplary parameters of interest.

In another embodiment, the portable monitoring device may include one ormore audio sensors to monitor, determine and/or track the quality and/ortone of conversation between the user and others (with or without asimilarly-equipped device). In yet another embodiment, the sameinteractions between “friends” in a social network are specificallymonitored, cataloged and/or tracked.

In another embodiment, the portable monitoring device may interact withother devices through wireless communication to allow the user to play agame. For example, a pair of devices may be bumped together to initiatea game-like contest between the two users based on their step counts.Other examples may include animated virtual “fight” sequences betweenavatars placed on both devices. The users' devices may buzz, glow, etc.to notify the users that there is another contestable device nearby. Itmay do this for “friends” only or for anyone with an appropriate device.The contest may follow deterministic behavior or probabilistic behavior.

Again, in the context of the aforementioned embodiments, the devices mayconsist of the portable monitoring device according to any of theembodiments described herein and a second device, which may be a secondportable monitoring device or may be a different device (for example, amobile phone or tablet).

Notably, the use of “wireless” also extends to visual and/or magneticdetection and identification of compatible devices. For instance, theinvention may have a magnetic field sensor that can recognize themagnetic signature of compatible devices.

As noted above, the portable monitoring device may contain useridentification credentials (for example, stored in memory). Here, theportable monitoring device may enable the user to authenticate tocertain services or be automatically recognized. For instance, a userattempting to login to “www.fitbit.com” from a computer may beautomatically routed to the appropriate, designated, associated and/orcorrect user account and bypass authentication steps by placing theportable monitoring device into a specific authentication mode, or byvirtue of proximity detection of the device by the computer.

Likewise, the user may be automatically identified when approachingother devices that are configured to recognize the invention. Forinstance, the user may be recognized as she approaches a weight scalethat communicates with her device.

In situations where a plurality of portable monitoring devices are“present” in a single location (e.g., a family where each family memberowns a device), it may be useful for each portable monitoring device tohave a unique identifier so that a user associated with which device maybe determined. The indicator may be chosen by the user or it may bepreprogrammed onto the device. The indicator may be turned off ordisabled by the user. In one embodiment, when the device is placedstationary (e.g., display side up on a table surface) and thensubsequently moved or jolted, it displays the indicator. The display mayalso show other content such as motivational messages, general messages,animations, graphics, etc. In another embodiment, the device may showthe same or similar information through an input from the user throughthe user interface. In yet another embodiment, the device may displaythe same or similar information when the device is coupled to or removedfrom a specific fixture (e.g., charger) or put in proximity of fixture(e.g., RF beacon, magnetic source). The unique identifiers comprise aspecific color shown on a multicolor LED, color/animation sequence,nickname/keyword, word sequence, vibration sequence, custom avatar, orimage.

In one embodiment, the portable monitoring device has a RFID and/or NFCtag embedded in it. The tag may be either read or write. If the tag isread-only, then it stores some static information about the device, forinstance, a unique identification number. If the tag is read and write,then either a NFC writer device can write to it or an onboard MCU isconnected to the tag and can write to it. If another device equippedwith a NFC reader (e.g., smartphone) is brought in proximity to thedevice, then an app (e.g., Fitbit mobile app) launches automatically andtransfers data with the device (e.g., communication to and fromwww.fitbit.com). If the application “knows” the portable monitoringdevice (e.g., it is set up to recognize the device), then it transfersdata. If the application sees that the device belongs to someone else,it offers the ability to “friend” that person or establish a similarrelationship. It may also show some personal information about the user,who may be a human or nonhuman animal. If the application sees that thedevice is an unknown device, it offers a setup option. Data transferfunctionality may occur directly through the RFID tag itself (e.g., theRFID antenna is connected to an EEPROM that the onboard microprocessorcan write to) or the RFID/NFC may act as just an automatic discoverymechanism while data transfer is over another system such as ANT,Bluetooth, Zigbee, Wifi, etc.

In one embodiment, the portable monitoring device is a multi-protocollocal area network (LAN) to wide area network (WAN) gateway where localdevices may be Bluetooth, ANT, Zigbee, etc. and the gateway communicatesto the internet via or over a communication path (for example, a cellphone network, WLAN, etc.). The portable monitoring device may operateas an “open hotspot” so that no user setup is required for subsequentdevices. For instance, a user may have elsewhere established a networkaccount (e.g., www.fitbit.com or another website) to the device (e.g.,Fitbit Tracker) through its unique device ID, then the deviceautomatically recognizes compatible devices and sends their data to thecorrect account and location. The data may go directly to thedestination or through an intermediary first. Destinations orintermediaries could be other devices or a network service (e.g.,www.fitbit.com). The “original” portable monitoring device toaccount/location link setup could have been done as part of a userinitiated setup process or could have been pre-configured as part of thepurchasing or acquisition process at the manufacturer or anotherintermediary. The following is an additional exemplary embodiment:

-   -   A user owns a Garmin ANT device that is set up to sync data to        Garmin's website. She then acquires the current invention. Once        she connects the invention to the internet, the Garmin device        can automatically send its data to Garmin's website through the        invention without any further setup. The invention could also        send the data to Garmin's website via an intermediary website        (e.g., www.fitbit.com).        The user may also turn off and on (disable or enable) the        ability for data destinations to receive the data.

The communication circuitry of the portable monitoring device mayprovide for one-way or two-way communication to, for example, facilitateor provide input of data and/or commands. Indeed, where the deviceincludes two-way communications, the communication circuitry facilitatesor provides data or command transmission to and from peripheral devicesand/or the Internet. Thus, in certain embodiments, the communicationcircuitry facilitates or provides external connectivity to, for example,the Internet and/or remote or local external devices and/or appliances.

Where the communication circuitry provides one-way or two-waycommunication to the Internet and/or (remote or local) external devicesand/or appliances, the portable monitoring device may upload data and/orcommands to and/or download data and/or commands from, for example,selected websites, health professionals, trainers, weight or healthoriented monitoring groups/organizations or specialists, and/or the like(hereinafter collectively “third party” or “third parties”). In thisway, the portable monitoring device may manually or automaticallyprovide data to such third parties. The portable monitoring device mayalso receive data and/or instructions/comments, for example, health ortraining guidance or feedback via the device. For example, where theportable monitoring device provides data (for example, activity levels,steps and/or sleep quality) to one or more third party devices orwebsites, such third parties (for example, health professionals ortrainers) may monitor and/or provide feedback based on such data. Inthis way, such third party or parties may provide periodic, continuousand/or intermittent monitoring and/or feedback, notwithstanding theuser/patient is substantially remote or distant from such third parties,or where significant monitoring of the user/patient is inconvenient ornot feasible (for example, due to costs or locations).

The communication circuitry may also facilitate programming of theportable monitoring device, for example, programming the device toacquire selected data (via enabling and/or disabling selected sensors)and/or calculate, monitor and/or determine selected physiologicalparameters (via enabling or disabling the processing circuitryaccordingly). The programming of the portable monitoring device may bevia the user or third party. In this way, for example, a third party maycustomize or tailor the acquisition of physiological data based on theuser, the situation (for example, physical condition of the user), andthe acquisition of desired information.

In certain embodiments, the portable monitoring device may also operate,program and/or control local external devices and/or appliances. Forexample, the communication circuitry of the device may also function asa relay or hub to provide or facilitate communication for externaldevices to each other or to the Internet. For example, the device mayconnect to the Internet via WLAN but also be equipped with an ANT radio.An ANT device may communicate with the device to transmit its data tothe Internet through the WLAN of the device (and vice versa). Moreover,where the communication circuitry is equipped with Bluetooth, otherBluetooth-enabled devices (for example, mobile or smart telephones) thatcome within suitable or effective reach or range, the device maytransmit data to or receive data from such Bluetooth-enable deviceand/or the Internet through the network of the mobile or smarttelephones. Indeed, data from another device may also be transmitted tothe device and stored (and vice versa) or subsequently transmitted at alater time.

In another preferred embodiment, the portable monitoring device is asingle protocol or multi-protocol wireless bridge that may relay, store,and/or display data from compatible wireless devices. Compatible devicesneed not be activity monitors. For example, a weight scale may transmitdata of the user's weight to the device and it may display an historicalgraph of the user's weight. Data transfer may be bidirectional. Data maybe stored and later transferred through the device's wirelesscommunication circuitry to other services such as the Internet.

In a preferred embodiment, when the portable monitoring device is placedstationary (e.g., display side up or display side down on a tablesurface), it attempts wireless communication with nearby compatiblewireless devices. Indeed, in several embodiments a wirelesscommunication attempt is initiated by the portable monitoring devicethrough one or more gestures detected by the motion sensor. Forinstance, the device may be placed display side up on a surface for afixed time interval then subsequently flipped to the display side downposition.

As mentioned above, the portable monitoring device may include a userinterface having a display. In one embodiment, the display iscustomizable in that the information and content of the display may becustomized by the user. The user may configure the types of information“screens” as they show up on the device and in which order. This may beachieved through configuration on the device or an external application(e.g., a settings manager on www.fitbit.com). As indicated above, theportable monitoring device, in addition to monitoring, calculatingand/or determining of one or more activity and physiological parameters(based on or using data from resident sensors), may receive web contentfor display on the user interface of the portable monitoring device. Thefollowing are examples of the types and/or content of information thatmay be provided to the user.

-   -   Historical and current graphs and/or data of user activity        and/or foods consumed and/or sleep data that are measured by the        device and/or stored remotely (e.g., fitbit.com);    -   Historical graphs and data of user weight and/or body fat data        measured by a weight scale and transferred to the device (either        over the Internet or by the scale itself);    -   Historical graphs and data of other user-tracked data measured        by the device or stored remotely. Examples include heart rate,        blood pressure, arterial stiffness, blood glucose levels,        cholesterol, duration of TV watching, duration of video game        play, mood, etc.;    -   Physiological data corresponding to average or norms, for        example, for comparison purposes wherein, in one embodiment, the        user's physiological data is compared to or contrasted with        average physiological data (for example, on an age, gender or        condition basis (for example, a pregnant women's physiological        data is compared with typical physiological data based on stage,        size and age));    -   “Mash-up” data pertaining to user's physiologic data and user's        water intake—for example, correlations of (i) hydration levels        to manually logged water consumption and (ii) hydration levels        to automatically measured water consumption via a “smart” water        bottle (e.g., Camelbak flow meter hydration gauge system);    -   “Mash-up” data pertaining to user's physiologic data and user's        sleep—for example, correlations of (i) heart rate to blood        pressure and (ii) body weight and/or fat to sleep time, patterns        and/or quality;    -   “Mash-up” data pertaining to user's physiologic data and user's        activity—for example, correlations of (i) hydration to activity        levels and (ii) heart rate and/or variability to activity levels        and/or patterns;    -   “Mash-up” data pertaining to physiologic data and potentially        related external events such as correlations of (i) user's body        weight and/or fat to ambient environment for example, geography,        temperature and/or weather, (ii) user's heart rate and/or blood        pressure to financial markets (for example, S&P 500, NASDAQ or        Dow Jones); here the data analysis of the user's biometric or        physiologic data is correlated to web content and/or external        devices that are in communication with the biometric monitoring        device;    -   Coaching and/or dieting data based on one or more of the user's        current weight, weight goals, food intake, activity, sleep, and        other data;    -   User progress toward weight, activity, sleep, and/or other        goals;    -   Summary statistics, graphics, badges, and/or metrics (e.g.,        “grades”) to describe the aforementioned data;    -   The aforementioned data displayed for the user and his/her        “friends” with similar devices and/or tracking methods;    -   Social content such as Twitter feeds, instant messaging, and/or        Facebook updates;    -   Other online content such as newspaper articles, horoscopes,        stock, sports or weather reports, RSS feeds, comics, crossword        puzzles, classified advertisements, and websites; and    -   Motivational messages, system messages (e.g., battery status,        “sync data” notifier), device communications that may be similar        to social content (e.g., the device may communicate greetings to        the user like “Hello”, “Good Morning”), and/or other messages        similar in content to a fortune cookie; and    -   Email messages and calendar schedules; and    -   Clock and/or stop watch.

For the avoidance of doubt, it should be understood that these examplesare provided for illustration and are not intended to limit theinventions, including the scope of data that may be transmitted,received, or displayed by the device, nor any intermediate processingthat may employed during such transfer and display.

Notably, selected content may be delivered according to differentcontexts. For example, in the morning, motivational messaging may bedisplayed along with the user's sleep data from the previous night. Inthe evening, a daily summary of the day's activities may be displayed.Notably, sleep and activity may be monitored and derived from otherdevices, manual log entries on a website, etc.—not the presentinvention. Such information, however, may be communicated to, forexample, the user and/or the Internet via the device.

Furthermore, in several embodiments the device has a motivational avatarwhose state is dependent on user activity. Nominally, the avatar may bea “flower” or plant that grows and shrinks as a function of the durationand intensity of a user's physical activity over a period of severalhours. In a preferred embodiment, the device may be programmed with acustomizable avatar and the behavior of the avatar may change over timeas the user interacts with the device. The device may also be programmedwith optional games and interventions to help the user meet goals.

Programming or customizing may be implemented via the user interface, anexternal device via the communication circuitry (for example, via wiredor wireless connection to a computer or personal computing device)and/or through a web interface (e.g., www.fitbit.com) on the userinterface of the device. Similarly, the firmware loaded on the devicemay be updated and configured by the user through the communicationcircuitry (for example, via wireless connection). Indeed, functions andfeatures of the device (for example, certain sensors or data processing)as described here may also be modified, enabled and/or disabled (forexample, on an individual basis or global basis).

In a preferred embodiment, the portable monitoring device may be mountedto different parts of the user and/or exercise equipment to performfunctions that are specific or adapted to its location. For example,when mounted on the torso, the portable monitoring device may monitorthe user's steps, distance, pace, calorie burn, activity intensity,altitude gain/loss, stair steps, etc. based on data from the motionsensor and altitude sensor using one set of algorithms. When theportable monitoring device is mounted to the user's foot, the device maymonitor the same or similar parameters using possibly a different sensorconfiguration and another set of algorithms. Additionally, it theportable monitoring device may monitor the impact accelerations presenton the foot to determine 1) if the user is running with soft or hardfootfalls or 2) if the user is running on a soft or hard surface (e.g.,the amount of time spent running on concrete vs. grass). When theportable monitoring device is mounted to the wrist, arm, hand, it mayperform sleep tracking functionality, swimming functionality, ambulatoryactivity functionality, or stress biofeedback functionality using, inpart or wholly, a heart rate and/or respiratory rate sensor and/orgalvanic skin response (GSR) sensor—the determination of which mode ofoperation may be determined automatically through mode or motion sensordata, or the user may place the device into the appropriate modemanually.

Note that in the case of the hand, the portable monitoring device may beheld rather than mounted. In a preferred embodiment, a gesture of thedevice mostly contained in the orthogonal plane to the gravitationalvector may be used as a user interface mechanism (e.g., to navigate amenu system). Because the gesture is mostly orthogonal to gravity, itmay be reliably distinguished between other motions that produce signalsthat are mostly parallel to gravity (e.g., walking, running, andjumping). In a preferred embodiment, the gesture is similar to the flickof the wrist (as typically produced when dealing cards or throwing aflying disc) and/or the reverse motion toward the body. Due to the fastmotion of this gesture, it may be readily distinguished from othermotions that are orthogonal to gravity (e.g., hip rotations). In lieu ofor in combination with the preceding gesture, the device may incorporateanother gesture wherein the direction of gravity relative to one or moreaxes of an accelerometer begins in a first orientation, then is moved toa second orientation that is roughly 90° degrees different, thenreturned to the first orientation. For example, in the case of a devicewith a 3D accelerometer, a 1 g acceleration may be sensed on oneaccelerometer axis in the first orientation (and close to 0 g on bothother axes), then go to near 0 g in the second orientation (and close to1 g on one of the other axes), then return to 1 g when the device isreturned to the first orientation. Because such rotations do not occuroften in natural use of an activity monitoring device, it may be readilyused as a gesture for user interactions. Notably, these gestures may berecognized with a variety of sensors other than or in combination withan accelerometer such as a magnetic compass, gyroscope, mechanicalswitch, etc.

In regards to swim tracking, the use of inertial and/or magnetic sensors(e.g., compasses) may be used to determine the lap count of the user.Speed and distance may subsequently be calculated based on a configuredlap length. When the device is placed on a bicycle hub or wheel, it mayperform bicycle activity tracking (e.g., cadence, calorie burn).

As mentioned before, the portable monitoring device may also transmitits data to a secondary display device so that the user may see itsoutput in real-time. In another embodiment, the device may communicatewith another activity monitoring device (e.g., ANT-enabled heart ratemonitor) either directly or indirectly through a wireless bridge (e.g.,a smart phone that routes data from one device to another, or betweenboth). When the portable monitoring device is placed on a free weight orweight training apparatus, it may perform functions related to weighttraining (e.g., tracking repetitions, sets, time betweenrepetitions/sets, type of exercise, form.).

In another aspect of the present inventions, the portable monitoringdevice may select which sensors and algorithms to use based on itsmounting condition. For instance, if the device is mounted to the arm,it uses an optical heart rate monitor to calculate calorie burn. If theportable monitoring device is mounted to the torso, it uses anaccelerometer to calculate calorie burn. In this example, the device maydetermine its location partially or wholly from the quality of the heartrate signal: if no signal is present, the device is not mounted to thearm. In this example, mounting conditions may furthermore be determinedthrough the design of the mounting fixtures. For example, the fixturesmay have reflective and anti-reflective materials which enable thedevice to optically distinguish between a mounting to the torso and/orto the foot. Notably, the optical module in the portable monitoringdevice may incorporate an array of emitters (e.g., LEDs) and/or an arrayof detectors (e.g., photodiodes, photodetectors, photoresistors) whereinthe emitters/detectors have different locations relative to the body andmay be used to find the best signal locations while reducing powerconsumption (by, for instance, dimming or turning off someemitters/detectors).

Other embodiments may employ fixtures and devices which have magnetsand/or magnetic sensors (e.g., Hall effect sensors), RF beacons andtransmitters, buttons, electrical contacts, proximity sensors, etc. Theportable monitoring device may also be equipped with a magnetic fieldsensor which is used to recognize the magnetic signature of fixtures andother devices.

Note that an optical system for heart rate sensing may also be used tosense respiratory rate. The two signals are modulated into a singlephotoplethysmography (PPG) signal. When applied to stress biofeedback,the PPG sensor may provide information on heart rate, heart ratevariability, and respiratory rate. This may be supplemented orsupplanted with information from a GSR sensor in the device. The sameinformation may be used with or without the motion sensor to assist insleep tracking, specifically as they relate to sleep onset and sleepstages. In other embodiments of the invention, the device may alsoinclude ambient light sensing, noise sensing, as well as all possiblecombinations of the previously mentioned sensors. When mounted to thearm and used in ambulatory activity monitoring, the PPG-derivedinformation may be displayed to the user on a secondary device such as awrist-mounted watch.

Notably, when the portable monitoring device is mounted to the torso, acombination of motion sensing and altitude sensing may be used to detectperiods in which the user is sitting, standing, and lying down.

The portable monitoring device may also include one or moreenvironmental sensors to detect, measure and/or sense ambientenvironmental conditions. For example, the one or more environmentalsensors may detect, measure and/or sense ambient pressure, temperature,sound, light, humidity, location and/or atmosphere. In this manner, theportable monitoring device may monitor, in addition to user activity,other metrics related to noise pollution/levels, weather, UV exposure,lighting conditions, and/or air quality.

In another embodiment, the portable monitoring device may monitor the RFsignature of nearby devices in order to determine its location. Forinstance, a home may have a set of Bluetooth or ANT devices that arebeaconing to form a signature for the home. Indeed, the portablemonitoring device may monitor the signature in order to determine itslocation in the home. The portable monitoring device may also determineits location directly from a GPS-enabled device that has a compatiblecommunication protocol with the invention (e.g., a Bluetooth enabledsmart phone). The portable monitoring device may also use a barometricpressure sensor to determine the user's altitude. (Obviously, othercommunication protocols can be used too, like RFID, WiFi, NFC, etc).

The portable monitoring device may include a rechargeable battery orultracapacitor to provide electrical power to the circuitry and otherelements of the portable monitoring device. In one embodiment, theenergy storage element (for example, battery or ultracapacitor) mayobtain energy from, for example, a charger.

In one embodiment, the portable monitoring device includes an active orpassive energy harvesting circuitry wherein the energy acquired,obtained and/or generated by the circuitry is employed to immediatelypower the device or stored in, for example a rechargeable battery orultracapacitor for later use by the a rechargeable battery orultracapacitor.

With references to FIGS. 15A-15C, the energy harvesting circuitryportable monitoring device may convert the movement of the user toenergy, using, for example, elements, circuitry and/or techniques whichgenerate energy in response to the movement of the user (for example,moving magnets and/or piezoelectric elements). In this regard, theportable monitoring device passively “harvests” or converts the kineticenergy supplied by the user (for example, via movement) to charge thebattery or ultracapacitor, supplement such charge, and/or immediatelypower the device. For example, the device may incorporate piezoelectricelements that generate current in response to user motion and associatedcircuitry to rectify and store said current. In lieu of or incombination, the device may incorporate a magnet that may move within aninductive coil such that motion of the device induces a current throughthe coil. The coil may be shaped as a cylinder, for example, to capturethe motion shocks during user movement or ambulation, or it may beshaped as a toroid so that the magnet may move easily under otherconditions.

In another embodiment, the portable monitoring device includes circuitryto “harvest” or acquire the energy from signals in the surroundingatmosphere. In this embodiment, the energy harvesting circuitry convertsthe energy of or in those signals to charge the battery orultracapacitor, supplement such charge, and/or immediately power thedevice.

As mentioned above, at least a portion of the portable monitoring device(including the one or more altitude sensors and/or motion sensors) maybe affixed to the user during operation wherein the portable monitoringdevice includes a physical size and/or shape that facilitates couplingto the user, for example, the body of the user (such as, for example,arm, wrist, angle, waist and/or foot) and allows the user to performnormal or typical user activities (including, for example, exercise ofall kinds and type) without hindering the user from performing suchactivities. (See, for example, FIGS. 16A-16D and 17). The portablemonitoring device may include a mechanism (for example, a clip, strapand/or tie) that facilitates coupling or affixing the device to the userduring such normal or typical user activities. A base station mayfacilitate interface to a second device (for example, computer) and/orrecharging of the battery. (See, for example, FIG. 16D).

It should be noted that the term “circuit” may mean, among other things,a single component or a multiplicity of components (whether inintegrated circuit form or otherwise), which are active and/or passive,and which are coupled together to provide or perform a desired function.The term “circuitry” may mean, among other things, a circuit (whetherintegrated or otherwise), a group of such circuits, one or moreprocessors, one or more state machines, one or more processorsimplementing software, one or more gate arrays, programmable gate arraysand/or field programmable gate arrays, or a combination of one or morecircuits (whether integrated or otherwise), one or more state machines,one or more processors, one or more processors implementing software,one or more gate arrays, programmable gate arrays and/or fieldprogrammable gate arrays. The term “data” may mean, among other things,a current or voltage signal(s) whether in an analog or a digital form,which may be a single bit (or the like) or multiple bits (or the like).

It should be further noted that the various circuits and circuitrydisclosed herein may be described using computer aided design tools andexpressed (or represented), as data and/or instructions embodied invarious computer-readable media, for example, in terms of theirbehavioral, register transfer, logic component, transistor, layoutgeometries, and/or other characteristics. Formats of files and otherobjects in which such circuit expressions may be implemented include,but are not limited to, formats supporting behavioral languages such asC, Verilog, and HLDL, formats supporting register level descriptionlanguages like RTL, and formats supporting geometry descriptionlanguages such as GDSII, GDSIII, GDSIV, CIF, MEBES and any othersuitable formats and languages. Computer-readable media in which suchformatted data and/or instructions may be embodied include, but are notlimited to, non-volatile storage media in various forms (e.g., optical,magnetic or semiconductor storage media) and carrier waves that may beused to transfer such formatted data and/or instructions throughwireless, optical, or wired signaling media or any combination thereof.Examples of transfers of such formatted data and/or instructions bycarrier waves include, but are not limited to, transfers (uploads,downloads, e-mail, etc.) over the Internet and/or other computernetworks via one or more data transfer protocols (e.g., HTTP, FTP, SMTP,etc.). The present inventions are also directed to such representationof the circuitry described herein, and/or techniques implementedthereby, and, as such, are intended to fall within the scope of thepresent inventions.

Indeed, when received within a computer system via one or morecomputer-readable media, such data and/or instruction-based expressionsof the above described circuits may be processed by a processing entity(e.g., one or more processors) within the computer system in conjunctionwith execution of one or more other computer programs including, withoutlimitation, net-list generation programs, place and route programs andthe like, to generate a representation or image of a physicalmanifestation of such circuits. Such representation or image maythereafter be used in device fabrication, for example, by enablinggeneration of one or more masks that are used to form various componentsof the circuits in a device fabrication process.

Moreover, the various circuits and circuitry, as well as techniques,disclosed herein may be represented via simulations and simulationinstruction-based expressions using computer aided design, simulationand/or testing tools. The simulation of the various sensors, processingcircuitry, user interface, transmitter circuitry and/or receivercircuitry of the present inventions (regardless of combination orpermutation of sensors, processing circuitry, transmitter circuitryand/or receiver circuitry), including the processes or techniquesimplemented thereby, may be implemented by a computer system whereincharacteristics and operations of such circuitry, and techniquesimplemented thereby, are simulated, imitated, replicated, analyzedand/or predicted via a computer system. The present inventions are alsodirected to such simulations and testing of the inventive portablemonitoring device (or portions thereof including, for example, thevarious sensors, processing circuitry, user interface, input/outputcircuitry (although not illustrated—the input/output circuitry may bediscrete circuitry or circuitry which is integrated into the processingcircuitry), transmitter circuitry and/or receiver circuitry), and/ortechniques implemented thereby, and, as such, are intended to fallwithin the scope of the present inventions. The computer-readable mediaand data corresponding to such simulations and/or testing tools are alsointended to fall within the scope of the present inventions.

The term “calculate” and other forms (i.e., calculating, calculated andcalculation) in the claims means, among other things, calculate,assesses, determine and/or estimate and other forms thereof. Inaddition, the term “calorie burn” in the claims means, among otherthings, calorie burn or calorie expenditure and/or energy burn or energyexpenditure—or the like.

Further, in the claims, the phrase “data which is representative of achange in altitude” means data which is representative of an altitude ofthe user (absolute altitude) and data which is representative of achange in altitude (relative altitude). Further, in the claims, thephrase “a change in altitude” means a change in altitude or height.Moreover, for the avoidance of doubt, in the claims, the term “flightsof stairs” means “flights of stairs”, “floors” and the like.

Notably, the terms “first,” “second,” and the like, herein do not denoteany order, quantity, or importance, but rather are used to distinguishone element from another. Moreover, in the claims, the terms “a” and“an” herein do not denote a limitation of quantity, but rather denotethe presence of at least one of the referenced item.

What is claimed is:
 1. An activity monitoring system comprising: aportable activity monitoring device comprising: a housing having aphysical size and shape that is adapted to couple to the body of theuser, a motion sensor, disposed in the housing, to generate data whichis representative of motion of the user, and an altitude sensor,disposed in the housing, to generate data which is representative of thechange in altitude of the user; and a display to output data which isrepresentative of a badge, wherein the badge is representative of anachievement computed using data from the motion sensor and/or altitudesensor.
 2. The activity monitoring system of claim 1 wherein the badgecorresponds to one of a swimming, distance, sleep and motion activitymetric.
 3. The activity monitoring system of claim 1 wherein the badgecorresponds to one of a biking, location and walking/running activitymetric.
 4. The activity monitoring system of claim 1 wherein the displayis disposed in and/or affixed to the housing.
 5. The activity monitoringsystem of claim 1 wherein the display to further output data which isrepresentative of the progress towards a user activity goal calculatedusing data from the motion sensor and/or altitude sensor.
 6. Theactivity monitoring system of claim 5 wherein the user activity goalcorresponds to one of a swimming, distance, sleep and motion activitymetric.
 7. The activity monitoring system of claim 5 wherein the useractivity goal corresponds to one of a biking, location andwalking/running activity metric.
 8. The activity monitoring system ofclaim 1 further including processing circuitry to compute the data whichis representative of the badge.
 9. The activity monitoring system ofclaim 8 wherein the processing circuitry is disposed in the housing ofthe portable activity monitoring device.
 10. An activity monitoringsystem comprising: a portable activity monitoring device comprising: ahousing having a physical size and shape that is adapted to couple tothe body of the user, a motion sensor, disposed in the housing, togenerate data which is representative of motion of the user, an altitudesensor, disposed in the housing, to generate data which isrepresentative of the change in altitude of the user, and one or morephysiological sensors to generate data which is representative of aphysiological condition of the user; and a display to output data whichis representative of a badge wherein the badge is representative of anachievement and is generated using data from the one or morephysiological sensors.
 11. The activity monitoring system of claim 10wherein the badge corresponds to heart rate and/or pulse rate of theuser.
 12. The activity monitoring system of claim 10 wherein the displayis disposed in and/or affixed to the housing.
 13. The activitymonitoring system of claim 10 further including processing circuitry tocompute the data which is representative of the badge.
 14. The activitymonitoring system of claim 13 wherein the processing circuitry isdisposed in the housing of the portable activity monitoring device. 15.The activity monitoring system of claim 10 wherein the one or morephysiological sensors are disposed in the housing of the portableactivity monitoring device.
 16. An activity monitoring systemcomprising: a portable activity monitoring device comprising: a housinghaving a physical size and shape that is adapted to couple to the bodyof the user, a motion sensor, disposed in the housing, to generate datawhich is representative of motion of the user, an altitude sensor,disposed in the housing, to generate data which is representative of thechange in altitude of the user, and one or more physiological sensors togenerate data which is representative of a physiological condition ofthe user; and a display to output data which is representative of: afirst badge, wherein the first badge is representative of a motionand/or an altitude activity achievement computed using data from themotion sensor and/or altitude sensor, and a second badge wherein thebadge is representative of a physiological achievement and is generatedusing data from the one or more physiological sensors.
 17. The activitymonitoring system of claim 16 wherein the first badge corresponds to oneof a swimming, distance, sleep and motion activity metric.
 18. Theactivity monitoring system of claim 16 wherein the first badgecorresponds to one of a biking, location and walking/running activitymetric.
 19. The activity monitoring system of claim 16 wherein thedisplay is disposed in and/or affixed to the housing.
 20. The activitymonitoring system of claim 16 further including processing circuitry tocompute the data which is representative of the first badge or secondbadge.
 21. The activity monitoring system of claim 20 wherein theprocessing circuitry is disposed in the housing of the portable activitymonitoring device.
 22. The activity monitoring system of claim 16wherein the second badge corresponds to heart rate and/or pulse rate ofthe user.
 23. The activity monitoring system of claim 16 wherein the oneor more physiological sensors are disposed in the housing of theportable activity monitoring device.
 24. The activity monitoring systemof claim 16 further including processing circuitry to compute the datawhich is representative of the first and second badge.
 25. The activitymonitoring system of claim 24 wherein the processing circuitry isdisposed in the housing of the portable activity monitoring device.